The following preprints are provided here to allow for a deeper view of our research work, as well as to
promote the rapid dissemination of research results. Please consider, on the other hand, that these preprints
can differ from their published version in ways that may not be entirely negligible, and for this reason we
recommend you to refer to the published version whenever they have to be used or cited.

top## 2019

## Reliable discretisation of deterministic equations in Bayesian networks

## Credal sentential decision diagrams

## Modeling spatially dependent functional data via regression with differential regularization

## Hierarchical estimation of parameters in Bayesian networks

## Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding

## Using active learning to decrease probes for QoT estimation in optical networks

## Bernstein's socks, polynomial-time provable coherence and entanglement

## Online end-use energy disaggregation via jump linear models

## From location tracking to personalized eco-feedback: a framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors

## Identification of elasto-plastic and nonlinear fracture mechanics parameters of silver-plated copper busbars for photovoltaics

## Exploring the space of probabilistic sentential decision diagrams

## Kernelized identification of linear parameter-varying models with linear fractional representation

## A tutorial on machine learning for failure management in optical networks

## Reverse engineering creativity into interpretable neural networks

## Incremental alignment of metaphoric language model for poetry composition

## Finite-horizon integration for continuous-time identification: bias analysis and application to variable stiffness actuators

## Semialgebraic outer approximations for set-valued nonlinear filtering

## Performance-oriented model learning for data-driven MPC design

## Hybrid heuristic for the optimal design of photovoltaic installations considering mismatch loss effects

## Efficient feature selection using shrinkage estimators

## Compatibility, coherence and the RIP

## Desirability foundations of robust rational decision making

(2019). Reliable discretisation of deterministic equations in Bayesian networks. In *Proceedings of the 32nd International Flairs Conference*, AAAI Press.

@INPROCEEDINGS{supsi2019c,

title = {Reliable discretisation of deterministic equations in {B}ayesian networks},

publisher = {AAAI Press},

booktitle = {Proceedings of the 32nd International Flairs Conference},

author = {Antonucci, A.},

year = {2019},

url = {https://www.flairs-32.info}

}

Downloadtitle = {Reliable discretisation of deterministic equations in {B}ayesian networks},

publisher = {AAAI Press},

booktitle = {Proceedings of the 32nd International Flairs Conference},

author = {Antonucci, A.},

year = {2019},

url = {https://www.flairs-32.info}

}

(2019). Credal sentential decision diagrams. In *Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications (ISIPTA '19)*.

@INPROCEEDINGS{supsi2019b,

title = {Credal sentential decision diagrams},

booktitle = {Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications ({ISIPTA} '19)},

author = {Antonucci, A. and Facchini, A. and Mattei, L.},

year = {2019},

url = {http://isipta2019.ugent.be}

}

Downloadtitle = {Credal sentential decision diagrams},

booktitle = {Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications ({ISIPTA} '19)},

author = {Antonucci, A. and Facchini, A. and Mattei, L.},

year = {2019},

url = {http://isipta2019.ugent.be}

}

(2019). Modeling spatially dependent functional data via regression with differential regularization. *Journal of Multivariate Analysis* **170**, pp. 275–295.

@ARTICLE{azzimonti2018a,

title = {Modeling spatially dependent functional data via regression with differential regularization},

journal = {Journal of Multivariate Analysis},

volume = {170},

author = {Arnone, E. and Azzimonti, L. and Nobile, F. and Sangalli, L.M.},

pages = {275-295},

year = {2019},

doi = {10.1016/j.jmva.2018.09.006}

}

Downloadtitle = {Modeling spatially dependent functional data via regression with differential regularization},

journal = {Journal of Multivariate Analysis},

volume = {170},

author = {Arnone, E. and Azzimonti, L. and Nobile, F. and Sangalli, L.M.},

pages = {275-295},

year = {2019},

doi = {10.1016/j.jmva.2018.09.006}

}

(2019). Hierarchical estimation of parameters in Bayesian networks. *Computational Statistics and Data Analysis* **137**, pp. 67–91.

@ARTICLE{azzimonti2019a,

title = {Hierarchical estimation of parameters in {B}ayesian networks},

journal = {Computational Statistics and Data Analysis},

volume = {137},

author = {Azzimonti, L. and Corani, G. and Zaffalon, M.},

pages = {67--91},

year = {2019},

doi = {10.1016/j.csda.2019.02.004}

}

Downloadtitle = {Hierarchical estimation of parameters in {B}ayesian networks},

journal = {Computational Statistics and Data Analysis},

volume = {137},

author = {Azzimonti, L. and Corani, G. and Zaffalon, M.},

pages = {67--91},

year = {2019},

doi = {10.1016/j.csda.2019.02.004}

}

(2019). Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding. *Technometrics*, pp. 1–27.

@ARTICLE{azzimontid2019a,

title = {Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding},

journal = {Technometrics},

publisher = {Taylor & Francis},

author = {Azzimonti, D. and Ginsbourger, D. and Rohmer, J. and Idier, D.},

pages = {1--27},

year = {2019},

doi = {10.1080/00401706.2018.1562987}

}

Downloadtitle = {Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding},

journal = {Technometrics},

publisher = {Taylor & Francis},

author = {Azzimonti, D. and Ginsbourger, D. and Rohmer, J. and Idier, D.},

pages = {1--27},

year = {2019},

doi = {10.1080/00401706.2018.1562987}

}

(2019). Using active learning to decrease probes for QoT estimation in optical networks. In , Optical Society of America, pp. Th1H.1.

@INPROCEEDINGS{azzimontid2019b,

title = {Using active learning to decrease probes for {QoT} estimation in optical networks},

journal = {Optical Fiber Communication Conference ({OFC}) 2019},

publisher = {Optical Society of America},

author = {Azzimonti, D. and Rottondi, C. and Tornatore, M.},

pages = {Th1H.1},

year = {2019},

doi = {10.1364/OFC.2019.Th1H.1}

}

Downloadtitle = {Using active learning to decrease probes for {QoT} estimation in optical networks},

journal = {Optical Fiber Communication Conference ({OFC}) 2019},

publisher = {Optical Society of America},

author = {Azzimonti, D. and Rottondi, C. and Tornatore, M.},

pages = {Th1H.1},

year = {2019},

doi = {10.1364/OFC.2019.Th1H.1}

}

(2019). Bernstein's socks, polynomial-time provable coherence and entanglement. In De Bock, J., de Campos, C., de Cooman, G., Quaeghebeur, E., Wheeler, G. (Eds), *ISIPTA '19: Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications*, PMLR, JMLR.org.

@INPROCEEDINGS{zaffalon2019b,

title = {Bernstein's socks, polynomial-time provable coherence and entanglement},

editor = {De Bock, J. and de Campos, C. and de Cooman, G. and Quaeghebeur, E. and Wheeler, G.},

publisher = {JMLR.org},

series = {PMLR},

booktitle = {{ISIPTA };'19: Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications},

author = {Benavoli, A. and Facchini, A. and Zaffalon, M.},

year = {2019}

}

Downloadtitle = {Bernstein's socks, polynomial-time provable coherence and entanglement},

editor = {De Bock, J. and de Campos, C. and de Cooman, G. and Quaeghebeur, E. and Wheeler, G.},

publisher = {JMLR.org},

series = {PMLR},

booktitle = {{ISIPTA };'19: Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications},

author = {Benavoli, A. and Facchini, A. and Zaffalon, M.},

year = {2019}

}

(2019). Online end-use energy disaggregation via jump linear models. *Control Engineering Practice* **89**, pp. 30–42.

@ARTICLE{piga2019b,

title = {Online end-use energy disaggregation via jump linear models},

journal = {Control Engineering Practice},

volume = {89},

author = {Breschi, V. and Piga, D. and Bemporad, A.},

pages = {30--42},

year = {2019},

doi = {10.1016/j.conengprac.2019.05.011}

}

Downloadtitle = {Online end-use energy disaggregation via jump linear models},

journal = {Control Engineering Practice},

volume = {89},

author = {Breschi, V. and Piga, D. and Bemporad, A.},

pages = {30--42},

year = {2019},

doi = {10.1016/j.conengprac.2019.05.011}

}

(2019). From location tracking to personalized eco-feedback: a framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors. *Travel Behaviour and Society* **14**, pp. 43–56.

@ARTICLE{mangili2019a,

title = { From location tracking to personalized eco-feedback: a framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors},

journal = {Travel Behaviour and Society},

volume = {14},

author = {Bucher, D. and Mangili, F. and Cellina, F. and Bonesana, C. and Jonietz, D. and Raubal, M.},

pages = {43-56},

year = {2019},

doi = {10.1016/j.tbs.2018.09.005}

}

Downloadtitle = { From location tracking to personalized eco-feedback: a framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors},

journal = {Travel Behaviour and Society},

volume = {14},

author = {Bucher, D. and Mangili, F. and Cellina, F. and Bonesana, C. and Jonietz, D. and Raubal, M.},

pages = {43-56},

year = {2019},

doi = {10.1016/j.tbs.2018.09.005}

}

(2019). Identification of elasto-plastic and nonlinear fracture mechanics parameters of silver-plated copper busbars for photovoltaics. *Engineering Fracture Mechanics* **205**, pp. 439–454.

@ARTICLE{piga2019d,

title = {Identification of elasto-plastic and nonlinear fracture mechanics parameters of silver-plated copper busbars for photovoltaics},

journal = {Engineering Fracture Mechanics},

volume = {205},

author = {Carollo, V. and Piga, D. and Borri, C. and Paggi, M.},

pages = {439--454},

year = {2019},

url = {https://www.sciencedirect.com/science/article/pii/S001379441830451X?via%3Dihub}

}

Downloadtitle = {Identification of elasto-plastic and nonlinear fracture mechanics parameters of silver-plated copper busbars for photovoltaics},

journal = {Engineering Fracture Mechanics},

volume = {205},

author = {Carollo, V. and Piga, D. and Borri, C. and Paggi, M.},

pages = {439--454},

year = {2019},

url = {https://www.sciencedirect.com/science/article/pii/S001379441830451X?via%3Dihub}

}

(2019). Exploring the space of probabilistic sentential decision diagrams. In *Proceedings of the 3rd Tractable Probabilistic Modeling Workshop, 36th Interna- tional Conference on Machine Learning, Long Beach, California*.

@INPROCEEDINGS{supsi2019a,

title = {Exploring the space of probabilistic sentential decision diagrams},

booktitle = {Proceedings of the 3rd Tractable Probabilistic Modeling Workshop, 36th Interna- {t}ional Conference on Machine Learning, Long Beach, California},

author = {Mattei, L. and Soares, D.L. and Antonucci, A. and Mau\`a, D.D. and Facchini, A.},

year = {2019},

url = {https://sites.google.com/view/icmltpm2019/home}

}

Downloadtitle = {Exploring the space of probabilistic sentential decision diagrams},

booktitle = {Proceedings of the 3rd Tractable Probabilistic Modeling Workshop, 36th Interna- {t}ional Conference on Machine Learning, Long Beach, California},

author = {Mattei, L. and Soares, D.L. and Antonucci, A. and Mau\`a, D.D. and Facchini, A.},

year = {2019},

url = {https://sites.google.com/view/icmltpm2019/home}

}

(2019). Kernelized identification of linear parameter-varying models with linear fractional representation. In *2019 European Control Conference (ecc)*, Naples, Italy.

@INPROCEEDINGS{piga2019e,

title = {Kernelized identification of linear parameter-varying models with linear fractional representation},

address = {Naples, Italy},

booktitle = {2019 European Control Conference ({e}cc)},

author = {Mejari, M. and Piga, D. and Toth, R. and Bemporad, A.},

year = {2019}

}

Downloadtitle = {Kernelized identification of linear parameter-varying models with linear fractional representation},

address = {Naples, Italy},

booktitle = {2019 European Control Conference ({e}cc)},

author = {Mejari, M. and Piga, D. and Toth, R. and Bemporad, A.},

year = {2019}

}

(2019). A tutorial on machine learning for failure management in optical networks. *Journal of Lightwave Technology*.

@ARTICLE{corani2019b,

title = {A tutorial on machine learning for failure management in optical networks},

journal = {Journal of Lightwave Technology},

author = {Musumeci, F. and Rottondi, C.E.M. and Corani, G. and Shahkarami, S. and Cugini, F. and Tornatore, M.},

year = {2019},

doi = {10.1109/JLT.2019.2922586}

}

Downloadtitle = {A tutorial on machine learning for failure management in optical networks},

journal = {Journal of Lightwave Technology},

author = {Musumeci, F. and Rottondi, C.E.M. and Corani, G. and Shahkarami, S. and Cugini, F. and Tornatore, M.},

year = {2019},

doi = {10.1109/JLT.2019.2922586}

}

(2019). Reverse engineering creativity into interpretable neural networks. In *Future of Information and Communications* **70**, Springer series "Lecture Notes in Networks and Systems", pp. 235–247.

@INPROCEEDINGS{oita2019innGenuity,

title = {Reverse engineering creativity into interpretable neural networks},

publisher = {Springer series "Lecture Notes in Networks and Systems"},

volume = {70},

booktitle = {Future of Information and Communications},

author = {Oita, M.},

pages = {235-247},

year = {2019},

doi = {10.1007/978-3-030-12385-7_19}

}

Downloadtitle = {Reverse engineering creativity into interpretable neural networks},

publisher = {Springer series "Lecture Notes in Networks and Systems"},

volume = {70},

booktitle = {Future of Information and Communications},

author = {Oita, M.},

pages = {235-247},

year = {2019},

doi = {10.1007/978-3-030-12385-7_19}

}

(2019). Incremental alignment of metaphoric language model for poetry composition. In *Computing Conference*, Springer, "Advances in Intelligent Systems and Computing".

@INPROCEEDINGS{oita2019poetryComposition,

title = {Incremental alignment of metaphoric language model for poetry composition},

publisher = {Springer, "Advances in Intelligent Systems and Computing"},

booktitle = {Computing Conference},

author = {Oita, M.},

year = {2019}

}

Downloadtitle = {Incremental alignment of metaphoric language model for poetry composition},

publisher = {Springer, "Advances in Intelligent Systems and Computing"},

booktitle = {Computing Conference},

author = {Oita, M.},

year = {2019}

}

(2019). Finite-horizon integration for continuous-time identification: bias analysis and application to variable stiffness actuators. *International Journal of Control*, pp. 1–14.

@ARTICLE{piga2019c,

title = {Finite-horizon integration for continuous-time identification: bias analysis and application to variable stiffness actuators},

journal = {International Journal of Control},

publisher = {Taylor & Francis},

author = {Piga, D.},

pages = {1--14},

year = {2019},

doi = {10.1080/00207179.2018.1557348}

}

Downloadtitle = {Finite-horizon integration for continuous-time identification: bias analysis and application to variable stiffness actuators},

journal = {International Journal of Control},

publisher = {Taylor & Francis},

author = {Piga, D.},

pages = {1--14},

year = {2019},

doi = {10.1080/00207179.2018.1557348}

}

(2019). Semialgebraic outer approximations for set-valued nonlinear filtering. In *2019 European Control Conference (ECC)*, Naples, Italy.

@INPROCEEDINGS{piga2019f,

title = {Semialgebraic outer approximations for set-valued nonlinear filtering},

address = {Naples, Italy},

booktitle = {2019 European Control Conference ({ECC})},

author = {Piga, D. and Benavoli, A.},

year = {2019}

}

Downloadtitle = {Semialgebraic outer approximations for set-valued nonlinear filtering},

address = {Naples, Italy},

booktitle = {2019 European Control Conference ({ECC})},

author = {Piga, D. and Benavoli, A.},

year = {2019}

}

(2019). Performance-oriented model learning for data-driven MPC design. *IEEE Control Systems Letters* **3**(3), pp. 577–582.

@ARTICLE{piga2019a,

title = {Performance-oriented model learning for data-driven {MPC} design},

journal = {{IEEE} Control Systems Letters},

volume = {3},

author = {Piga, D. and Forgione, M. and Formentin, S. and Bemporad, A.},

number = {3},

pages = {577 - 582},

year = {2019},

doi = {10.1109/LCSYS.2019.2913347}

}

Downloadtitle = {Performance-oriented model learning for data-driven {MPC} design},

journal = {{IEEE} Control Systems Letters},

volume = {3},

author = {Piga, D. and Forgione, M. and Formentin, S. and Bemporad, A.},

number = {3},

pages = {577 - 582},

year = {2019},

doi = {10.1109/LCSYS.2019.2913347}

}

(2019). Hybrid heuristic for the optimal design of photovoltaic installations considering mismatch loss effects. *Computers & Operations Research* **108**, pp. 112–120.

@ARTICLE{corani2019a,

title = {Hybrid heuristic for the optimal design of photovoltaic installations considering mismatch loss effects},

journal = {Computers & Operations Research},

volume = {108},

author = {Salani, M. and Corbellini, G. and Corani, G.},

pages = {112--120},

year = {2019},

doi = {10.1016/j.cor.2019.04.009}

}

Downloadtitle = {Hybrid heuristic for the optimal design of photovoltaic installations considering mismatch loss effects},

journal = {Computers & Operations Research},

volume = {108},

author = {Salani, M. and Corbellini, G. and Corani, G.},

pages = {112--120},

year = {2019},

doi = {10.1016/j.cor.2019.04.009}

}

(2019). Efficient feature selection using shrinkage estimators. *Machine Learning*.

@ARTICLE{azzimonti2019b,

title = {Efficient feature selection using shrinkage estimators},

journal = {Machine Learning},

author = {Sechidis, K. and Azzimonti, L. and Pocock, A. and Corani, G. and Weatherall, J. and Brown, G.},

year = {2019},

doi = {10.1007/s10994-019-05795-1},

url = {https://doi.org/10.1007/s10994-019-05795-1}

}

Downloadtitle = {Efficient feature selection using shrinkage estimators},

journal = {Machine Learning},

author = {Sechidis, K. and Azzimonti, L. and Pocock, A. and Corani, G. and Weatherall, J. and Brown, G.},

year = {2019},

doi = {10.1007/s10994-019-05795-1},

url = {https://doi.org/10.1007/s10994-019-05795-1}

}

(2019). Compatibility, coherence and the RIP. In Destercke, S.,Denoeux, T., Gil, M. A., Grzegorzewski, P., Hryniewicz, O. (Ed), *Uncertainty Modelling in Data Science*, Advances in Intelligent Systems and Computing **832**, Springer, pp. 166–174.

@INCOLLECTION{zaffalon2018a,

title = {Compatibility, coherence and the {RIP}},

editor = {Destercke, S.,Denoeux, T., Gil, M. A., Grzegorzewski, P., Hryniewicz, O.},

publisher = {Springer},

series = {Advances in Intelligent Systems and Computing},

volume = {832},

booktitle = {Uncertainty Modelling in Data Science},

author = {Miranda, E., Zaffalon, M.},

pages = {166--174},

year = {2019},

doi = {10.1007/978-3-319-97547-4_22}

}

Downloadtitle = {Compatibility, coherence and the {RIP}},

editor = {Destercke, S.,Denoeux, T., Gil, M. A., Grzegorzewski, P., Hryniewicz, O.},

publisher = {Springer},

series = {Advances in Intelligent Systems and Computing},

volume = {832},

booktitle = {Uncertainty Modelling in Data Science},

author = {Miranda, E., Zaffalon, M.},

pages = {166--174},

year = {2019},

doi = {10.1007/978-3-319-97547-4_22}

}

(2019). Desirability foundations of robust rational decision making. *Synthese*.

@ARTICLE{zaffalon2019a,

title = {Desirability foundations of robust rational decision making},

journal = {Synthese},

publisher = {Springer},

author = {Zaffalon, M. and Miranda, E.},

year = {2019},

doi = {10.1007/s11229-018-02010-x}

}

Downloadtitle = {Desirability foundations of robust rational decision making},

journal = {Synthese},

publisher = {Springer},

author = {Zaffalon, M. and Miranda, E.},

year = {2019},

doi = {10.1007/s11229-018-02010-x}

}

top## 2018

## A credal extension of independent choice logic

## Set-valued probabilistic sentential decision diagrams

## Fitting jump models

## Prediction error methods in learning jump ARMAX models

## Kalman filtering for energy disaggregation

## Jump model learning and filtering for energy end-use disaggregation

## Entropy-based pruning for learning Bayesian networks using BIC

## Reliable uncertain evidence modeling in Bayesian networks by credal networks

## Imaginary kinematics

## Regularized moving-horizon PWA regression for LPV system identification

## Energy disaggregation using piecewise affine regression and binary quadratic programming

## A bias-correction method for closed-loop identification of linear parameter-varying systems

## Direct data-driven control of constrained systems

## Approximate structure learning for large Bayesian networks

## Efficient learning of bounded-treewidth Bayesian networks from complete and incomplete data sets

## Towards direct data-driven model-free design of optimal controllers

(2018). A credal extension of independent choice logic. In *Proceedings of the 12th International Conference on Scalable Uncertainty Management (SUM 2018)*, pp. 35–49.

@INPROCEEDINGS{antonucci2018c,

title = {A credal extension of independent choice logic},

booktitle = {Proceedings of the 12th International Conference on Scalable Uncertainty Management ({SUM} 2018)},

author = {Antonucci, A. and Facchini, A.},

pages = {35-49},

year = {2018},

doi = {10.1007/978-3-030-00461-3_3},

url = {https://arxiv.org/abs/1806.08298}

}

Downloadtitle = {A credal extension of independent choice logic},

booktitle = {Proceedings of the 12th International Conference on Scalable Uncertainty Management ({SUM} 2018)},

author = {Antonucci, A. and Facchini, A.},

pages = {35-49},

year = {2018},

doi = {10.1007/978-3-030-00461-3_3},

url = {https://arxiv.org/abs/1806.08298}

}

(2018). Set-valued probabilistic sentential decision diagrams. In *Proceedings of the 5th Workshop on Probabilistic Logic Programming*.

@INPROCEEDINGS{antonucci2018d,

title = {Set-valued probabilistic sentential decision diagrams},

booktitle = {Proceedings of the 5th Workshop on Probabilistic Logic Programming},

author = {Antonucci, A. and Facchini, A.},

year = {2018},

url = {http://stoics.org.uk/plp/plp2018/}

}

Downloadtitle = {Set-valued probabilistic sentential decision diagrams},

booktitle = {Proceedings of the 5th Workshop on Probabilistic Logic Programming},

author = {Antonucci, A. and Facchini, A.},

year = {2018},

url = {http://stoics.org.uk/plp/plp2018/}

}

(2018). Fitting jump models. *Automatica* **96**, pp. 11–21.

@ARTICLE{piga2018e,

title = {Fitting jump models},

journal = {Automatica},

volume = {96},

author = {Bemporad, A. and Breschi, V. and Piga, D. and Boyd, S.},

pages = {11--21},

year = {2018},

doi = {10.1016/j.automatica.2018.06.022}

}

Downloadtitle = {Fitting jump models},

journal = {Automatica},

volume = {96},

author = {Bemporad, A. and Breschi, V. and Piga, D. and Boyd, S.},

pages = {11--21},

year = {2018},

doi = {10.1016/j.automatica.2018.06.022}

}

(2018). Prediction error methods in learning jump ARMAX models. In *2018 IEEE Conference on Decision and Control (cdc)*, pp. 2247–2252.

@INPROCEEDINGS{piga2018i,

title = {Prediction error methods in learning jump {ARMAX} models},

booktitle = {2018 {IEEE} Conference on Decision and Control ({c}dc)},

author = {Breschi, V. and Bemporad, A. and Piga, D. and Boyd, S.},

pages = {2247--2252},

year = {2018},

doi = {10.1109/CDC.2018.8619819}

}

Downloadtitle = {Prediction error methods in learning jump {ARMAX} models},

booktitle = {2018 {IEEE} Conference on Decision and Control ({c}dc)},

author = {Breschi, V. and Bemporad, A. and Piga, D. and Boyd, S.},

pages = {2247--2252},

year = {2018},

doi = {10.1109/CDC.2018.8619819}

}

(2018). Kalman filtering for energy disaggregation. In *Proc. of the 1st IFAC Workshop on Integrated Assessment Modelling for Environmental Systems* **51**(5), pp. 108–113.

@INPROCEEDINGS{piga2018b,

title = {Kalman filtering for energy disaggregation},

journal = {{IFAC}-{PapersOnLine}},

volume = {51},

booktitle = {Proc. {o}f the 1st {IFAC} Workshop on Integrated Assessment Modelling for Environmental Systems},

author = {Breschi, V. and Piga, D. and Bemporad, A.},

number = {5},

pages = {108--113},

year = {2018},

doi = {10.1016/j.ifacol.2018.06.219}

}

Downloadtitle = {Kalman filtering for energy disaggregation},

journal = {{IFAC}-{PapersOnLine}},

volume = {51},

booktitle = {Proc. {o}f the 1st {IFAC} Workshop on Integrated Assessment Modelling for Environmental Systems},

author = {Breschi, V. and Piga, D. and Bemporad, A.},

number = {5},

pages = {108--113},

year = {2018},

doi = {10.1016/j.ifacol.2018.06.219}

}

(2018). Jump model learning and filtering for energy end-use disaggregation. In *Proc. of the 18th IFAC Symposium on System Identification* **51**(15), pp. 275–280.

@INPROCEEDINGS{piga2018f,

title = {Jump model learning and filtering for energy end-use disaggregation},

volume = {51},

booktitle = {Proc. {o}f the 18th {IFAC} Symposium on System Identification},

author = {Breschi, V. and Piga, D. and Bemporad, A.},

number = {15},

pages = {275--280},

year = {2018},

doi = {10.1016/j.ifacol.2018.09.147}

}

Downloadtitle = {Jump model learning and filtering for energy end-use disaggregation},

volume = {51},

booktitle = {Proc. {o}f the 18th {IFAC} Symposium on System Identification},

author = {Breschi, V. and Piga, D. and Bemporad, A.},

number = {15},

pages = {275--280},

year = {2018},

doi = {10.1016/j.ifacol.2018.09.147}

}

(2018). Entropy-based pruning for learning Bayesian networks using BIC. *Artificial Intelligence* **260**, pp. 42–50.

@ARTICLE{deCampos2018a,

title = {Entropy-based pruning for learning {B}ayesian networks using {BIC}},

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pages = {42-50},

year = {2018},

doi = {10.1016/j.artint.2018.04.002}

}

(2018). Reliable uncertain evidence modeling in Bayesian networks by credal networks. In *Proceedings of the 31st International Flairs Conference*, AAAI Press, pp. 513–518.

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title = {Reliable uncertain evidence modeling in {B}ayesian networks by credal networks},

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pages = {513--518},

year = {2018},

url = { https://aaai.org/ocs/index.php/FLAIRS/FLAIRS18/paper/download/17696/16792}

}

(2018). Imaginary kinematics. In *Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence*, AUAI Press, pp. 104–113.

@INPROCEEDINGS{antonucci2018b,

title = {Imaginary kinematics},

publisher = {AUAI Press},

booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence},

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year = {2018},

url = {http://auai.org/uai2018/proceedings/papers/42.pdf}

}

(2018). Regularized moving-horizon PWA regression for LPV system identification. In *Proc. of the 18th IFAC Symposium on System Identification* **51**(15), pp. 1092–1097.

@INPROCEEDINGS{piga2018d,

title = {Regularized moving-horizon {PWA} regression for {LPV} system identification},

volume = {51},

booktitle = {Proc. {o}f the 18th {IFAC} Symposium on System Identification},

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Downloadtitle = {Regularized moving-horizon {PWA} regression for {LPV} system identification},

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number = {15},

pages = {1092--1097},

year = {2018},

doi = {10.1016/j.ifacol.2018.09.048}

}

(2018). Energy disaggregation using piecewise affine regression and binary quadratic programming. In *2018 IEEE Conference on Decision and Control (cdc)*, pp. 3116–3121.

@INPROCEEDINGS{piga2018h,

title = {Energy disaggregation using piecewise affine regression and binary quadratic programming},

booktitle = {2018 {IEEE} Conference on Decision and Control ({c}dc)},

author = {Mejari, M. and Naik, V.V. and Piga, D. and Bemporad, A.},

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pages = {3116--3121},

year = {2018},

doi = {10.1109/CDC.2018.8619175}

}

(2018). A bias-correction method for closed-loop identification of linear parameter-varying systems. *Automatica* **87**, pp. 128–141.

@ARTICLE{piga2018c,

title = {A bias-correction method for closed-loop identification of linear parameter-varying systems},

journal = {Automatica},

volume = {87},

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Downloadtitle = {A bias-correction method for closed-loop identification of linear parameter-varying systems},

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pages = {128--141},

year = {2018},

doi = {10.1016/j.automatica.2017.09.014}

}

(2018). Direct data-driven control of constrained systems. *IEEE Transactions on Control Systems Technology* **26**(4), pp. 1422–1429.

@ARTICLE{piga2018a,

title = {Direct data-driven control of constrained systems},

journal = {{IEEE} Transactions on Control Systems Technology},

volume = {26},

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year = {2018},

doi = {10.1109/TCST.2017.2702118}

}

(2018). Approximate structure learning for large Bayesian networks. *Machine Learning* **107**(8-10), pp. 1209–1227.

@ARTICLE{scanagatta2018b,

title = {Approximate structure learning for large {B}ayesian networks},

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(2018). Efficient learning of bounded-treewidth Bayesian networks from complete and incomplete data sets. *International Journal of Approximate Reasoning* **95**, pp. 152–166.

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title = {Efficient learning of bounded-treewidth {B}ayesian networks from complete and incomplete data sets},

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pages = {152--166},

year = {2018},

doi = {10.1016/j.ijar.2018.02.004}

}

(2018). Towards direct data-driven model-free design of optimal controllers. In *2018 European Control Conference (ecc)*, pp. 2836–2841.

@INPROCEEDINGS{piga2018g,

title = {Towards direct data-driven model-free design of optimal controllers},

booktitle = {2018 European Control Conference ({e}cc)},

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}

top## 2017

## A time-dependent PDE regularization to model functional data defined over spatio-temporal domains

## Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tables

## Demo abstract: extracting eco-feedback information from automatic activity tracking to promote energy-efficient individual mobility behavior

## Statistical comparison of classifiers through Bayesian hierarchical modelling

## Profiling the location and extent of musicians' pain using digital pain drawings

## Invariant NKT cells contribute to Chronic Lymphocytic Leukemia surveillance and prognosis

## Reliable knowledge-based adaptive tests by credal networks

## A unified framework for deterministic and probabilistic d-stability analysis of uncertain polynomial matrices

## Improved local search in Bayesian networks structure learning

## Technical gestures recognition by set-valued hidden Markov models with prior knowledge

## Full conglomerability, continuity and marginal extension

## Full conglomerability

## Axiomatising incomplete preferences through sets of desirable gambles

(2017). A time-dependent PDE regularization to model functional data defined over spatio-temporal domains. In Aneiros G., Bongiorno E.G., Cao R., Vieu P. (Ed), *Functional Statistics and Related Fields*, Springer International Publishing, pp. 41–44.

@INBOOK{azzimonti2017b,

title = {A time-dependent {PDE} regularization to model functional data defined over spatio-temporal domains},

editor = {Aneiros G., Bongiorno E.G., Cao R., Vieu P. },

publisher = {Springer International Publishing},

booktitle = {Functional Statistics and Related Fields},

author = {Arnone, E. and Azzimonti, L. and Nobile, F. and Sangalli, L.M.},

pages = {41--44},

year = {2017},

doi = {10.1007/978-3-319-55846-2_6}

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author = {Arnone, E. and Azzimonti, L. and Nobile, F. and Sangalli, L.M.},

pages = {41--44},

year = {2017},

doi = {10.1007/978-3-319-55846-2_6}

}

(2017). Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tables. In Raghavan, V., Aluru, S., Karypis, G., Miele, L., Wu, X. (Ed), *2017 IEEE 17th International Conference on Data Mining (ICDM)*, pp. 739–744.

@INPROCEEDINGS{azzimonti2017c,

title = {Hierarchical {M}ultinomial-{D}irichlet model for the estimation of conditional probability tables},

editor = {Raghavan, V., Aluru, S., Karypis, G., Miele, L., Wu, X.},

booktitle = {2017 {IEEE} 17th International Conference on Data Mining ({ICDM})},

author = {Azzimonti, L. and Corani, G. and Zaffalon, M.},

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pages = {739--744},

year = {2017},

doi = {10.1109/ICDM.2017.85}

}

(2017). Demo abstract: extracting eco-feedback information from automatic activity tracking to promote energy-efficient individual mobility behavior. In **33**(1), pp. 1–2.

@INPROCEEDINGS{mangili2017c,

title = {Demo abstract: extracting eco-feedback information from automatic activity tracking to promote energy-efficient individual mobility behavior},

journal = {Computer Science - Research and Development},

volume = {33},

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number = {1},

pages = {1-2},

year = {2017},

doi = {10.1007/s00450-017-0375-2}

}

(2017). Statistical comparison of classifiers through Bayesian hierarchical modelling. *Machine Learning* **106**(11), pp. 1817–1837.

@ARTICLE{corani2017a,

title = {Statistical comparison of classifiers through {B}ayesian hierarchical modelling},

journal = {Machine Learning},

volume = {106},

author = {Corani, G. and Benavoli, A. and Demšar, J. and Mangili, F. and Zaffalon, M.},

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number = {11},

pages = {1817--1837},

year = {2017},

doi = {10.1007/s10994-017-5641-9}

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(2017). Profiling the location and extent of musicians' pain using digital pain drawings. *PAIN Practice* **18**(1), pp. 53–66.

@ARTICLE{mangili2017a,

title = {Profiling the location and extent of musicians' pain using digital pain drawings},

journal = {{PAIN} Practice},

publisher = {Wiley},

volume = {18},

author = {Cruder, C. and Falla, D. and Mangili, F. and Azzimonti, L. and Ara\'ujo, L. and Williamon, A. and Barbero, M.},

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year = {2017},

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(2017). Invariant NKT cells contribute to Chronic Lymphocytic Leukemia surveillance and prognosis. *Blood* **129**(26), pp. 3440–3451.

@ARTICLE{azzimonti2017a,

title = {Invariant {NKT} cells contribute to {C}hronic {L}ymphocytic {L}eukemia surveillance and prognosis},

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volume = {129},

author = {Gorini, F. and Azzimonti, L. and Delfanti, G. and Scarf\`o, L. and Scielzo, C. and Bertilaccio, M.T. and Ranghetti, P. and Gulino, A. and Doglioni, C. and Napoli, A.D. and Capri, M. and Franceschi, C. and Calligaris-Cappio, F. and Ghia, P. and Bellone, M. and Dellabona, P. and Casorati, G. and de Lalla, C.},

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number = {26},

pages = {3440-3451},

year = {2017},

doi = {10.1182/blood-2016-11-751065}

}

(2017). Reliable knowledge-based adaptive tests by credal networks. In Antonucci, A., Cholvy, L., Papini, O. (Eds), *Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017*, Lecture Notes in Computer Science **10369**, Springer, Cham, pp. 282–291.

@INPROCEEDINGS{mangili2017b,

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doi = {10.1007/978-3-319-61581-3_26}

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(2017). A unified framework for deterministic and probabilistic d-stability analysis of uncertain polynomial matrices. *IEEE Transactions on Automatic Control* **PP**(99).

@ARTICLE{piga2017a,

title = {A unified framework for deterministic and probabilistic d-stability analysis of uncertain polynomial matrices},

journal = {{IEEE} Transactions on Automatic Control},

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number = {99},

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Downloadtitle = {A unified framework for deterministic and probabilistic d-stability analysis of uncertain polynomial matrices},

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year = {2017},

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(2017). Improved local search in Bayesian networks structure learning. In Antti Hyttinen, Joe Suzuki, Brandon Malone (Eds), *Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks (AMBN)*, Proceedings of Machine Learning Research **73**, PMLR, pp. 45–56.

@INPROCEEDINGS{scanagatta2017,

title = {Improved local search in {B}ayesian networks structure learning},

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series = {Proceedings of Machine Learning Research},

volume = {73},

booktitle = {Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks ({AMBN})},

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url = {http://proceedings.mlr.press/v73/scanagatta17a.html}

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(2017). Technical gestures recognition by set-valued hidden Markov models with prior knowledge. In Ferraro, M. B., Giordani, P., Vantaggi, B., Gagolewski, M., Gil, M. A., Grzegorzewski, P., Hryniewicz, O. (Eds), *Soft Methods for Data Science*, Advances in Intelligent Systems and Computing **456**, Springer, pp. 455–462.

@INCOLLECTION{antonucci2016a,

title = {Technical gestures recognition by set-valued hidden {M}arkov models with prior knowledge},

editor = {Ferraro, M. B. and Giordani, P. and Vantaggi, B. and Gagolewski, M. and Gil, M. A. and Grzegorzewski, P. and Hryniewicz, O.},

publisher = {Springer},

series = {Advances in Intelligent Systems and Computing},

volume = {456},

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series = {Advances in Intelligent Systems and Computing},

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pages = {455--462},

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doi = {10.1007/978-3-319-42972-4_56}

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(2017). Full conglomerability, continuity and marginal extension. In Ferraro, M. B., Giordani, P., Vantaggi, B., Gagolewski, M., Gil, M. A., Grzegorzewski, P., Hryniewicz, O. (Eds), *Soft Methods for Data Science*, Advances in Intelligent Systems and Computing **456**, Springer, pp. 355–362.

@INCOLLECTION{zaffalon2017a,

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publisher = {Springer},

series = {Advances in Intelligent Systems and Computing},

volume = {456},

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(2017). Full conglomerability. *Journal of Statistical Theory and Practice* **11**(4), pp. 634–669.

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(2017). Axiomatising incomplete preferences through sets of desirable gambles. *Journal of Artificial Intelligence Research* **60**, pp. 1057–1126.

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title = {Axiomatising incomplete preferences through sets of desirable gambles},

journal = {Journal of Artificial Intelligence Research},

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journal = {Journal of Artificial Intelligence Research},

volume = {60},

author = {Zaffalon, M. and Miranda, E.},

pages = {1057--1126},

year = {2017},

doi = {10.1613/jair.5230}

}

top## 2016

## The multilabel naive credal classifier

## Exploiting fitness apps for sustainable mobility - challenges deploying the GoEco! App

## Evaluating interval-valued influence diagrams

## Joint analysis of multiple algorithms and performance measures

## Learning extended tree augmented naive structures

## Air pollution prediction via multi-label classification

## Hierarchical Bayesian LASSO for a negative binomial regression

## Computational study of the fluid-dynamics in carotids before and after endarterectomy

## A prior near-ignorance Gaussian process model for nonparametric regression

## Adaptive testing by Bayesian networks with application to language assessment

## Hidden Markov models with set-valued parameters

## Conformity and independence with coherent lower previsions

## Bayesian network data imputation with application to survival tree analysis

## Learning treewidth-bounded bayesian networks with thousands of variables

(2016). The multilabel naive credal classifier. *International Journal of Approximate Reasoning* **83**, pp. 320–336.

@ARTICLE{antonucci2016c,

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Downloadtitle = {The multilabel naive credal classifier},

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(2016). Exploiting fitness apps for sustainable mobility - challenges deploying the GoEco! App. In *Proceedings of the 2016 conference ICT for Sustainability*, Advances in Computer Science Research, pp. 89–98.

@INPROCEEDINGS{mangili2016c,

title = {Exploiting fitness apps for sustainable mobility - challenges deploying the {GoEco}! App},

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(2016). Evaluating interval-valued influence diagrams. *International Journal of Approximate Reasoning* **80**, pp. 393–411.

@ARTICLE{antonucci2016b,

title = {Evaluating interval-valued influence diagrams},

journal = {International Journal of Approximate Reasoning},

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author = {Caba\~nas, R. and Antonucci, A. and Cano, A. and G\'omez-Olmedo, M.},

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Downloadtitle = {Evaluating interval-valued influence diagrams},

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pages = {393-411},

year = {2016},

doi = {10.1016/j.ijar.2016.05.004}

}

(2016). Joint analysis of multiple algorithms and performance measures. *New Generation Computing*, pp. 1–18.

@ARTICLE{deCampos2016,

title = {Joint analysis of multiple algorithms and performance measures},

journal = {New Generation Computing},

author = {de Campos, C.P. and Benavoli, A.},

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year = {2016},

doi = {10.1007/s00354-016-0005-8},

url = {http://people.idsia.ch/~alessio/decampos-benavoli-ngc2016.pdf}

}

Downloadtitle = {Joint analysis of multiple algorithms and performance measures},

journal = {New Generation Computing},

author = {de Campos, C.P. and Benavoli, A.},

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year = {2016},

doi = {10.1007/s00354-016-0005-8},

url = {http://people.idsia.ch/~alessio/decampos-benavoli-ngc2016.pdf}

}

(2016). Learning extended tree augmented naive structures. *International Journal of Approximate Reasoning.* **68**, pp. 153–163.

@ARTICLE{decampos2015a,

title = {Learning extended tree augmented naive structures},

journal = {International Journal of Approximate Reasoning.},

volume = {68},

author = {de Campos, C.P. and Corani, G. and Scanagatta, M. and Cuccu, M. and Zaffalon, M.},

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year = {2016},

doi = {10.1016/j.ijar.2015.04.006}

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Downloadtitle = {Learning extended tree augmented naive structures},

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pages = {153--163},

year = {2016},

doi = {10.1016/j.ijar.2015.04.006}

}

(2016). Air pollution prediction via multi-label classification. *Environmental Modelling & Software* **80**, pp. 259–264.

@ARTICLE{corani2016a,

title = {Air pollution prediction via multi-label classification},

journal = {Environmental Modelling & Software},

volume = {80},

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pages = {259--264},

year = {2016},

doi = {10.1016/j.envsoft.2016.02.030}

}

Downloadtitle = {Air pollution prediction via multi-label classification},

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volume = {80},

author = {Corani, G. and Scanagatta, M.},

pages = {259--264},

year = {2016},

doi = {10.1016/j.envsoft.2016.02.030}

}

(2016). Hierarchical Bayesian LASSO for a negative binomial regression. *Journal of Statistical Computation and Simulation*.

@ARTICLE{shuaiFu2015a,

title = {Hierarchical {B}ayesian {LASSO} for a negative binomial regression},

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doi = {10.1080/00949655.2015.1106541}

}

Downloadtitle = {Hierarchical {B}ayesian {LASSO} for a negative binomial regression},

journal = {Journal of Statistical Computation and Simulation},

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year = {2016},

doi = {10.1080/00949655.2015.1106541}

}

(2016). Computational study of the fluid-dynamics in carotids before and after endarterectomy. *Journal of Biomechanics* **49**(1), pp. 26–38.

@ARTICLE{azzimonti2016a,

title = {Computational study of the fluid-dynamics in carotids before and after endarterectomy},

journal = {Journal of Biomechanics},

editor = {Elsevier},

volume = {49},

author = {Guerciotti, B. and Vergara, C. and Azzimonti, L. and Forzenigo, L. and Buora, A. and Biondetti, P. and Domanin, M.},

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year = {2016},

doi = {10.1016/j.jbiomech.2015.11.009}

}

Downloadtitle = {Computational study of the fluid-dynamics in carotids before and after endarterectomy},

journal = {Journal of Biomechanics},

editor = {Elsevier},

volume = {49},

author = {Guerciotti, B. and Vergara, C. and Azzimonti, L. and Forzenigo, L. and Buora, A. and Biondetti, P. and Domanin, M.},

number = {1},

pages = {26--38},

year = {2016},

doi = {10.1016/j.jbiomech.2015.11.009}

}

(2016). A prior near-ignorance Gaussian process model for nonparametric regression. *International Journal of Approximate Reasoning*.

@ARTICLE{mangili2016b,

title = {A prior near-ignorance {G}aussian process model for nonparametric regression},

journal = {International Journal of Approximate Reasoning },

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year = {2016},

doi = {http://dx.doi.org/10.1016/j.ijar.2016.07.005}

}

Downloadtitle = {A prior near-ignorance {G}aussian process model for nonparametric regression},

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year = {2016},

doi = {http://dx.doi.org/10.1016/j.ijar.2016.07.005}

}

(2016). Adaptive testing by Bayesian networks with application to language assessment. In Micarelli, Alessandro, Stamper, John, Panourgia, Kitty (Eds), *Intelligent Tutoring Systems: 13th International Conference, ITS 2016, Zagreb, Croatia, June 7-10, 2016. Proceedings*, Lecture Notes in Computer Science, pp. 471–472.

@INPROCEEDINGS{mangili2016a,

title = {Adaptive testing by {B}ayesian networks with application to language assessment},

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booktitle = {Intelligent Tutoring Systems: 13th International Conference, {ITS} 2016, Zagreb, Croatia, June 7-10, 2016. Proceedings},

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pages = {471--472},

year = {2016},

url = {http://link.springer.com/content/pdf/bbm%3A978-3-319-39583-8%2F1.pdf}

}

(2016). Hidden Markov models with set-valued parameters. *Neurocomputing* **180**, pp. 94–107.

@ARTICLE{antonucci2015c,

title = {Hidden {M}arkov models with set-valued parameters},

journal = {Neurocomputing},

volume = {180},

author = {Mau\'a, D.D. and Antonucci, A. and de Campos, C.P.},

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pages = {94--107},

year = {2016},

doi = {doi:10.1016/j.neucom.2015.08.095}

}

(2016). Conformity and independence with coherent lower previsions. *International Journal of Approximate Reasoning* **78**, pp. 125–137.

@ARTICLE{zaffalon2016c,

title = {Conformity and independence with coherent lower previsions},

journal = {International Journal of Approximate Reasoning},

volume = {78},

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year = {2016},

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}

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journal = {International Journal of Approximate Reasoning},

volume = {78},

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year = {2016},

doi = {10.1016/j.ijar.2016.07.004}

}

(2016). Bayesian network data imputation with application to survival tree analysis. *Computational Statistics and Data Analysis* **93**, pp. 373–387.

@ARTICLE{zaffalon2015b,

title = {Bayesian network data imputation with application to survival tree analysis},

journal = {Computational Statistics and Data Analysis},

volume = {93},

author = {Rancoita, P.M.V. and Zaffalon, M. and Zucca, E. and Bertoni, F. and de Campos, C.P.},

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doi = {10.1016/j.csda.2014.12.008}

}

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pages = {373--387},

year = {2016},

doi = {10.1016/j.csda.2014.12.008}

}

(2016). Learning treewidth-bounded bayesian networks with thousands of variables. In Daniel D. Lee, Masashi Sugiyama, Ulrike V. Luxburg, Isabelle Guyon, Roman Garnett (Eds), *NIPS 2016: Advances in Neural Information Processing Systems 29*.

@INPROCEEDINGS{scanagatta2016a,

title = {Learning treewidth-bounded bayesian networks with thousands of variables},

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author = {Scanagatta, M. and Corani, G. and de Campos, C.P. and Zaffalon, M.},

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}

Downloadtitle = {Learning treewidth-bounded bayesian networks with thousands of variables},

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author = {Scanagatta, M. and Corani, G. and de Campos, C.P. and Zaffalon, M.},

year = {2016},

url = {http://papers.nips.cc/paper/6232-learning-treewidth-bounded-bayesian-networks-with-thousands-of-variables}

}

top## 2015

## The multilabel naive credal classifier

## Robust classification of multivariate time series by imprecise hidden Markov models

## Early classification of time series by hidden Markov models with set-valued parameters

## Blood flow velocity field estimation via spatial regression with PDE penalization

## Variable elimination for interval-valued influence diagrams

## Imprecision in machine learning and AI

## A Bayesian approach for comparing cross-validated algorithms on multiple data sets

## Bayesian hypothesis testing in machine learning

## Credal model averaging for classification: representing prior ignorance and expert opinions.

## Robust Bayesian model averaging for the analysis of presence–absence data

## A hierarchical Bayesian approach to negative binomial regression

## A prior near-ignorance Gaussian Process model for nonparametric regression

## New prior near-ignorance models on the simplex

## Reliable survival analysis based on the Dirichlet Process

## On the problem of computing the conglomerable natural extension

## Independent products in infinite spaces

## Conformity and independence with coherent lower previsions

## Learning Bayesian networks with thousands of variables

(2015). The multilabel naive credal classifier. In Augustin, T., Doria, S., Miranda, E., Quaeghebeur, E. (Eds), *ISIPTA '15: Proceedings of the Ninth International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 27–36.

@INPROCEEDINGS{antonucci2015a,

title = {The multilabel naive credal classifier},

editor = {Augustin, T. and Doria, S. and Miranda, E. and Quaeghebeur, E.},

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booktitle = {{ISIPTA} '15: Proceedings of the Ninth International Symposium on Imprecise Probability: Theories and Applications},

author = {Antonucci, A. and Corani, G.},

pages = {27--36},

year = {2015},

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Downloadtitle = {The multilabel naive credal classifier},

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pages = {27--36},

year = {2015},

url = {http://www.sipta.org/isipta15/data/paper/32.pdf}

}

(2015). Robust classification of multivariate time series by imprecise hidden Markov models. *International Journal of Approximate Reasoning* **56**(B), pp. 249–263.

@ARTICLE{antonucci2014c,

title = {Robust classification of multivariate time series by imprecise hidden {M}arkov models},

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volume = {56},

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year = {2015},

doi = {10.1016/j.ijar.2014.07.005}

}

Downloadtitle = {Robust classification of multivariate time series by imprecise hidden {M}arkov models},

journal = {International Journal of Approximate Reasoning},

volume = {56},

author = {Antonucci, A. and de Rosa, R. and Giusti, A. and Cuzzolin, F.},

number = {B},

pages = {249--263},

year = {2015},

doi = {10.1016/j.ijar.2014.07.005}

}

(2015). Early classification of time series by hidden Markov models with set-valued parameters . In *Proceedings of the NIPS Time Series Workshop 2015*.

@INPROCEEDINGS{antonucci2015d,

title = {Early classification of time series by hidden {M}arkov models with set-valued parameters },

booktitle = {Proceedings of the {NIPS} Time Series Workshop 2015},

author = {Antonucci, A. and Scanagatta, M. and Mau\`a, D.D. and de Campos, C.P.},

year = {2015},

url = {https://sites.google.com/site/nipsts2015/home}

}

Downloadtitle = {Early classification of time series by hidden {M}arkov models with set-valued parameters },

booktitle = {Proceedings of the {NIPS} Time Series Workshop 2015},

author = {Antonucci, A. and Scanagatta, M. and Mau\`a, D.D. and de Campos, C.P.},

year = {2015},

url = {https://sites.google.com/site/nipsts2015/home}

}

(2015). Blood flow velocity field estimation via spatial regression with PDE penalization. *Journal of the American Statistical Association, Theory and Methods Section* **110**(511), pp. 1057–1071.

@ARTICLE{azzimonti2015a,

title = {Blood flow velocity field estimation via spatial regression with {PDE} penalization},

journal = {Journal of the American Statistical Association, Theory and Methods Section},

volume = {110},

author = {Azzimonti, L. and Sangalli, L.M. and Secchi, P. and Domanin, M. and Nobile, F.},

number = {511},

pages = {1057--1071},

year = {2015},

doi = {10.1080/01621459.2014.946036}

}

Downloadtitle = {Blood flow velocity field estimation via spatial regression with {PDE} penalization},

journal = {Journal of the American Statistical Association, Theory and Methods Section},

volume = {110},

author = {Azzimonti, L. and Sangalli, L.M. and Secchi, P. and Domanin, M. and Nobile, F.},

number = {511},

pages = {1057--1071},

year = {2015},

doi = {10.1080/01621459.2014.946036}

}

(2015). Variable elimination for interval-valued influence diagrams. In Destercke, S., Denoeux, T. (Eds), *Proceedings of the 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2015)*, Lecture Notes in Computer Science **9161**, pp. 541–551.

@INCOLLECTION{antonucci2015b,

title = {Variable elimination for interval-valued influence diagrams},

editor = {Destercke, S. and Denoeux, T.},

series = {Lecture Notes in Computer Science},

volume = {9161},

booktitle = {Proceedings of the 13th European Conference on Symbolic and Quantitative Approaches to Reasoning {w}ith Uncertainty ({ECSQARU} 2015)},

author = {Caba\~nas, R. and Antonucci, A. and Cano, A. and G\'omez-Olmedo, M.},

pages = {541--551},

year = {2015},

chapter = {Symbolic and Quantitative Approaches to Reasoning with Uncertainty},

doi = {10.1007/978-3-319-20807-7_49}

}

Downloadtitle = {Variable elimination for interval-valued influence diagrams},

editor = {Destercke, S. and Denoeux, T.},

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author = {Caba\~nas, R. and Antonucci, A. and Cano, A. and G\'omez-Olmedo, M.},

pages = {541--551},

year = {2015},

chapter = {Symbolic and Quantitative Approaches to Reasoning with Uncertainty},

doi = {10.1007/978-3-319-20807-7_49}

}

(2015). Imprecision in machine learning and AI. In *The IEEE Intelligent Informatics Bulletin* **16**(1), IEEE Computer Society, pp. 20–23.

@INCOLLECTION{antonucci2015e,

title = {Imprecision in machine learning and {AI}},

publisher = {IEEE Computer Society},

volume = {16},

booktitle = {The {IEEE} Intelligent Informatics Bulletin},

author = {de Campos, C.P. and Antonucci, A.},

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number = {1},

pages = {20--23},

year = {2015},

url = {http://www.comp.hkbu.edu.hk/~cib/2015/Dec/iib_vol16no1.pdf}

}

(2015). A Bayesian approach for comparing cross-validated algorithms on multiple data sets. *Machine Learning* **100**(2), pp. 285–304.

@ARTICLE{corani2015b,

title = {A {B}ayesian approach for comparing cross-validated algorithms on multiple data sets},

journal = {Machine Learning},

volume = {100},

author = {Corani, G. and Benavoli, A.},

number = {2},

pages = {285--304},

year = {2015},

doi = {10.1007/s10994-015-5486-z}

}

Downloadtitle = {A {B}ayesian approach for comparing cross-validated algorithms on multiple data sets},

journal = {Machine Learning},

volume = {100},

author = {Corani, G. and Benavoli, A.},

number = {2},

pages = {285--304},

year = {2015},

doi = {10.1007/s10994-015-5486-z}

}

(2015). Bayesian hypothesis testing in machine learning. In *Proc. ECML PKDD 2015 (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases)*, pp. 199–202.

@INPROCEEDINGS{corani2015c,

title = {Bayesian hypothesis testing in machine learning},

booktitle = {Proc. {ECML} {PKDD} 2015 (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases)},

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Downloadtitle = {Bayesian hypothesis testing in machine learning},

booktitle = {Proc. {ECML} {PKDD} 2015 (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases)},

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pages = {199--202},

year = {2015},

doi = {10.1007/978-3-319-23461-8_13}

}

(2015). Credal model averaging for classification: representing prior ignorance and expert opinions.. *International Journal of Approximate Reasoning* **56**(B), pp. 264–277.

@ARTICLE{corani2014a,

title = {Credal model averaging for classification: representing prior ignorance and expert opinions.},

journal = {International Journal of Approximate Reasoning},

volume = {56},

author = {Corani, G. and Mignatti, A.},

number = {B},

pages = {264--277},

year = {2015},

doi = {10.1016/j.ijar.2014.07.001}

}

Downloadtitle = {Credal model averaging for classification: representing prior ignorance and expert opinions.},

journal = {International Journal of Approximate Reasoning},

volume = {56},

author = {Corani, G. and Mignatti, A.},

number = {B},

pages = {264--277},

year = {2015},

doi = {10.1016/j.ijar.2014.07.001}

}

(2015). Robust Bayesian model averaging for the analysis of presence–absence data. *Environmental and Ecological Statistics* **22**(3), pp. 513–534.

@ARTICLE{corani2015a,

title = {Robust {B}ayesian model averaging for the analysis of presence--absence data},

journal = {Environmental and Ecological Statistics},

volume = {22},

author = {Corani, G. and Mignatti, A.},

number = {3},

pages = {513--534},

year = {2015},

doi = {10.1007/s10651-014-0308-1}

}

Downloadtitle = {Robust {B}ayesian model averaging for the analysis of presence--absence data},

journal = {Environmental and Ecological Statistics},

volume = {22},

author = {Corani, G. and Mignatti, A.},

number = {3},

pages = {513--534},

year = {2015},

doi = {10.1007/s10651-014-0308-1}

}

(2015). A hierarchical Bayesian approach to negative binomial regression. *Methods and Applications of Analysis* **22**(4), pp. 409–428.

@ARTICLE{shuaiFu2015b,

title = {A hierarchical {B}ayesian approach to negative binomial regression},

journal = {Methods and Applications of Analysis},

volume = {22},

author = {Fu, S.},

number = {4},

pages = {409--428},

year = {2015},

doi = {10.4310/MAA.2015.v22.n4.a4}

}

Downloadtitle = {A hierarchical {B}ayesian approach to negative binomial regression},

journal = {Methods and Applications of Analysis},

volume = {22},

author = {Fu, S.},

number = {4},

pages = {409--428},

year = {2015},

doi = {10.4310/MAA.2015.v22.n4.a4}

}

(2015). A prior near-ignorance Gaussian Process model for nonparametric regression. In Augustin,T., Doria, S., Miranda, E., Quaeghebeur, E. (Eds), *ISIPTA '15: Proceedings of the Ninth International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 187–196.

@INPROCEEDINGS{mangili2015b,

title = {A prior near-ignorance {G}aussian {P}rocess model for nonparametric regression},

editor = {Augustin,T. and Doria, S. and Miranda, E. and Quaeghebeur, E.},

publisher = {SIPTA},

booktitle = {{ISIPTA} '15: Proceedings of the Ninth International Symposium on Imprecise Probability: Theories and Applications},

author = {Mangili, F.},

pages = {187--196},

year = {2015},

url = {http://www.sipta.org/isipta15/data/paper/15.pdf}

}

Downloadtitle = {A prior near-ignorance {G}aussian {P}rocess model for nonparametric regression},

editor = {Augustin,T. and Doria, S. and Miranda, E. and Quaeghebeur, E.},

publisher = {SIPTA},

booktitle = {{ISIPTA} '15: Proceedings of the Ninth International Symposium on Imprecise Probability: Theories and Applications},

author = {Mangili, F.},

pages = {187--196},

year = {2015},

url = {http://www.sipta.org/isipta15/data/paper/15.pdf}

}

(2015). New prior near-ignorance models on the simplex. *International Journal of Approximate Reasoning* **56**(Part B), pp. 278–306.

@ARTICLE{Mangili2014a,

title = {New prior near-ignorance models on the simplex},

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(2015). Reliable survival analysis based on the Dirichlet Process. *Biometrical Journal* **57**(6), pp. 1002–1019.

@ARTICLE{mangili2015a,

title = {Reliable survival analysis based on the {D}irichlet {P}rocess},

journal = {Biometrical Journal},

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number = {6},

pages = {1002--1019},

year = {2015},

doi = {10.1002/bimj.201500062}

}

(2015). On the problem of computing the conglomerable natural extension. *International Journal of Approximate Reasoning* **56**(A), pp. 1–27.

@ARTICLE{zaffalon2014b,

title = {On the problem of computing the conglomerable natural extension},

journal = {International Journal of Approximate Reasoning},

volume = {56},

author = {Miranda, E. and Zaffalon, M.},

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Downloadtitle = {On the problem of computing the conglomerable natural extension},

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year = {2015},

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}

(2015). Independent products in infinite spaces. *Journal of Mathematical Analysis and Applications* **425**(1), pp. 460–488.

@ARTICLE{zaffalon2015a,

title = {Independent products in infinite spaces},

journal = {Journal of Mathematical Analysis and Applications},

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year = {2015},

doi = {10.1016/j.jmaa.2014.12.049}

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year = {2015},

doi = {10.1016/j.jmaa.2014.12.049}

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(2015). Conformity and independence with coherent lower previsions. In Augustin,T., Doria, S., Miranda, E., Quaeghebeur, E. (Eds), *ISIPTA '15: Proceedings of the Ninth International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 197–206.

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(2015). Learning Bayesian networks with thousands of variables. In Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, Roman Garnett (Eds), *NIPS 2015: Advances in Neural Information Processing Systems 28*.

@INPROCEEDINGS{scanagatta2015a,

title = {Learning {B}ayesian networks with thousands of variables},

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Downloadtitle = {Learning {B}ayesian networks with thousands of variables},

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top## 2014

## Approximate credal network updating by linear programming with applications to decision making

## Probabilistic graphical models

## Decision making with hierarchical credal sets

## Mixed finite elements for spatial regression with PDE penalization

## Global sensitivity analysis for MAP inference in graphical models

## Extended tree augmented naive classifier

## Classification

## Credal Ensembles of Classifiers

## Trading off Speed and Accuracy in Multilabel Classification

## Kuznetsov independence for interval-valued expectations and sets of probability distributions: Properties and algorithms

## Hidden Markov models with imprecisely specified parameters

## Probabilistic inference in credal networks: new complexity results

## Transform both sides model: a parametric approach

## Min-BDeu and max-BDeu scores for learning Bayesian networks

## Comments on "Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks" by Andrés R. Masegosa and Serafín Moral

(2014). Approximate credal network updating by linear programming with applications to decision making. *International Journal of Approximate Reasoning* **58**, pp. 25–38.

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pages = {25--38},

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doi = {10.1016/j.ijar.2014.10.003}

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pages = {25--38},

year = {2014},

doi = {10.1016/j.ijar.2014.10.003}

}

(2014). Probabilistic graphical models. In Augustin, T., Coolen, F., de Cooman, G., Troffaes, M. (Eds), *Introduction to Imprecise Probabilities*, Wiley, pp. 207–229.

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title = {Probabilistic graphical models},

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author = {Antonucci, A. and de Campos, C.P. and Zaffalon, M.},

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pages = {207--229},

year = {2014},

chapter = {9},

doi = {10.1002/9781118763117.ch9}

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(2014). Decision making with hierarchical credal sets. In Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (Eds), *Information Processing and Management of Uncertainty in Knowledge-based Systems*, Communications in Computer and Information Science **444**, Springer, pp. 456–465.

@INPROCEEDINGS{antonucci2014b,

title = {Decision making with hierarchical credal sets},

editor = {Laurent, A. and Strauss, O. and Bouchon-Meunier, B. and Yager, R.R.},

publisher = {Springer},

series = {Communications in Computer and Information Science},

volume = {444},

booktitle = {Information Processing and Management of Uncertainty in Knowledge-{b}ased Systems},

author = {Antonucci, A. and Karlsson, A. and Sundgren, D.},

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(2014). Mixed finite elements for spatial regression with PDE penalization. *SIAM/ASA Journal on Uncertainty Quantification* **2**(1), pp. 305–335.

@ARTICLE{azzimonti2014a,

title = {Mixed finite elements for spatial regression with {PDE} penalization},

journal = {{SIAM/ASA} Journal on Uncertainty Quantification},

volume = {2},

author = {Azzimonti, L. and Nobile, F. and Sangalli, L.M. and Secchi, P.},

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number = {1},

pages = {305-335},

year = {2014},

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(2014). Global sensitivity analysis for MAP inference in graphical models. In Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D. , Weinberger, K.Q. (Eds), *Advances in Neural Information Processing Systems 27 (NIPS 2014)*, Curran Associates, Inc., pp. 2690–2698.

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url = {http://papers.nips.cc/paper/5472-global-sensitivity-analysis-for-map-inference-in-graphical-models.pdf}

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(2014). Extended tree augmented naive classifier. In van der Gaag, L., Feelders, A. (Ed), *PGM'14: Proceedings of the Seventh European Workshop on Probabilistic Graphical Models*, Lecture Notes in Artificial Intelligence **8754**, Springer, pp. 176–189.

@INPROCEEDINGS{decampos2014a,

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(2014). Classification. In Augustin,T., Coolen,F., de Cooman,G., Troffaes,M. (Eds), *Introduction to Imprecise Probabilities*, Wiley, pp. 261–285.

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(2014). Credal Ensembles of Classifiers. *Computational Statistics & Data Analysis* **71**, pp. 818–831.

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(2014). Trading off Speed and Accuracy in Multilabel Classification. In van der Gaag, L., Feelders, A. (Eds), *PGM'14: Proceedings of the Seventh European Workshop on Probabilistic Graphical Models*, Lecture Notes in Artificial Intelligence **8754**, Springer, pp. 145–159.

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(2014). Kuznetsov independence for interval-valued expectations and sets of probability distributions: Properties and algorithms. *International Journal of Approximate Reasoning* **55**(2), pp. 666–682.

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(2014). Hidden Markov models with imprecisely specified parameters. In *Proceedings of the Brazilian Conference on Intelligent Systems*.

@INPROCEEDINGS{antonucci2014d,

title = {Hidden {M}arkov models with imprecisely specified parameters},

booktitle = {Proceedings of the Brazilian Conference on Intelligent Systems},

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Downloadtitle = {Hidden {M}arkov models with imprecisely specified parameters},

booktitle = {Proceedings of the Brazilian Conference on Intelligent Systems},

author = {Mau\'a, D.D. and de Campos, C.P. and Antonucci, A.},

year = {2014}

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(2014). Probabilistic inference in credal networks: new complexity results. *Journal of Artifical Intelligence Research* **50**, pp. 603–637.

@ARTICLE{maua14jair,

title = {Probabilistic inference in credal networks: new complexity results},

journal = {Journal of Artifical Intelligence Research},

volume = {50},

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pages = {603--637},

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doi = {10.1613/jair.4355}

}

Downloadtitle = {Probabilistic inference in credal networks: new complexity results},

journal = {Journal of Artifical Intelligence Research},

volume = {50},

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pages = {603--637},

year = {2014},

doi = {10.1613/jair.4355}

}

(2014). Transform both sides model: a parametric approach. *Computational Statistics and Data Analysis* **71**, pp. 903–913.

@ARTICLE{decampos2013b,

title = {Transform both sides model: a parametric approach},

journal = {Computational Statistics and Data Analysis},

publisher = {Elsevier},

volume = {71},

author = {Polpo, A. and de Campos, C.P. and Sinha, D. and Lipsitz, S. and Lin, J.},

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year = {2014},

doi = {10.1016/j.csda.2013.07.023}

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Downloadtitle = {Transform both sides model: a parametric approach},

journal = {Computational Statistics and Data Analysis},

publisher = {Elsevier},

volume = {71},

author = {Polpo, A. and de Campos, C.P. and Sinha, D. and Lipsitz, S. and Lin, J.},

pages = {903--913},

year = {2014},

doi = {10.1016/j.csda.2013.07.023}

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(2014). Min-BDeu and max-BDeu scores for learning Bayesian networks. In van der Gaag, L., Feelders, A. (Eds), *PGM'14: Proceedings of the Seventh European Workshop on Probabilistic Graphical Models*, Lecture Notes in Artificial Intelligence **8754**, Springer, pp. 426–441.

@INPROCEEDINGS{scanagatta2014a,

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(2014). Comments on "Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks" by Andrés R. Masegosa and Serafín Moral. *International Journal of Approximate Reasoning* **55**(7), pp. 1597–1600.

@ARTICLE{zaffalon2014a,

title = {Comments on {"Imprecise} probability models for learning multinomial distributions from data. Applications to learning credal networks" by {A}ndr\'es {R}. Masegosa and {S}eraf\'in {M}oral},

journal = {International Journal of Approximate Reasoning},

volume = {55},

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Downloadtitle = {Comments on {"Imprecise} probability models for learning multinomial distributions from data. Applications to learning credal networks" by {A}ndr\'es {R}. Masegosa and {S}eraf\'in {M}oral},

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volume = {55},

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number = {7},

pages = {1597--1600},

year = {2014},

doi = {10.1016/j.ijar.2014.05.001}

}

top## 2013

## Approximating credal network inferences by linear programming

## An ensemble of Bayesian networks for multilabel classification

## CREDO: a military decision-support system based on credal networks

## Temporal data classification by imprecise dynamical models

## Nonlinear nonparametric mixed-effects models for unsupervised classification

## Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks

## Discovering subgroups of patients from DNA copy number data using NMF on compacted matrices

## A Bayesian network model for predicting pregnancy after in vitro fertilization

## Credal model averaging of logistic regression for modeling the distribution of marmot burrows

## Objective way to support embryo transfer: a probabilistic decision

## Prognostic impact of monocyte count at presentation in mantle cell lymphoma

## New prior near-ignorance models on the simplex

## On the complexity of strong and epistemic credal networks

## On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables

## Conglomerable coherent lower previsions

## Conglomerable coherence

## Computing the conglomerable natural extension

## Probability and time

(2013). Approximating credal network inferences by linear programming. In van der Gaag, L. C. (Ed), *Proceedings of the 12th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty*, Lecture Notes in Artificial Intelligence **7958**, Springer, Berlin Heidelberg, pp. 13–25.

@INPROCEEDINGS{antonucci2013a,

title = {Approximating credal network inferences by linear programming},

editor = {van der Gaag, L. C.},

publisher = {Springer},

address = {Berlin Heidelberg},

series = {Lecture Notes in Artificial Intelligence},

volume = {7958},

booktitle = {Proceedings of the 12th European Conference on Symbolic and Quantitative Approaches to Reasoning {w}ith Uncertainty},

author = {Antonucci, A. and de Campos, C.P. and Huber, D. and Zaffalon, M.},

pages = {13--25},

year = {2013},

doi = {10.1007/978-3-642-39091-3_2}

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Downloadtitle = {Approximating credal network inferences by linear programming},

editor = {van der Gaag, L. C.},

publisher = {Springer},

address = {Berlin Heidelberg},

series = {Lecture Notes in Artificial Intelligence},

volume = {7958},

booktitle = {Proceedings of the 12th European Conference on Symbolic and Quantitative Approaches to Reasoning {w}ith Uncertainty},

author = {Antonucci, A. and de Campos, C.P. and Huber, D. and Zaffalon, M.},

pages = {13--25},

year = {2013},

doi = {10.1007/978-3-642-39091-3_2}

}

(2013). An ensemble of Bayesian networks for multilabel classification. In *Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI-13)*, pp. 1220–1225.

@INPROCEEDINGS{antonucci2013d,

title = {An ensemble of {B}ayesian networks for multilabel classification},

booktitle = {Proceedings of the 23rd International Joint Conference on Artificial Intelligence ({IJCAI}-13)},

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pages = {1220--1225},

year = {2013}

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Downloadtitle = {An ensemble of {B}ayesian networks for multilabel classification},

booktitle = {Proceedings of the 23rd International Joint Conference on Artificial Intelligence ({IJCAI}-13)},

author = {Antonucci, A. and Corani, G. and Mau\'a, D.D. and Gabaglio, S.},

pages = {1220--1225},

year = {2013}

}

(2013). CREDO: a military decision-support system based on credal networks. In *Proceedings of the 16th Conference on Information Fusion (FUSION 2013)*, pp. 1–8.

@INPROCEEDINGS{antonucci2013c,

title = {{CREDO}: a military decision-support system based on credal networks},

booktitle = {Proceedings of the 16th Conference on Information Fusion ({FUSION} 2013)},

author = {Antonucci, A. and Huber, D. and Zaffalon, M. and Luginbuehl, P. and Chapman, I. and Ladouceur, R.},

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(2013). Temporal data classification by imprecise dynamical models. In Cozman, F.G., Denoeux, T., Destercke, S., Seidenfeld, T. (Eds), *ISIPTA '13: Proceedings of the Eighth International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 13–22.

@INPROCEEDINGS{antonucci2013b,

title = {Temporal data classification by imprecise dynamical models},

editor = {Cozman, F.G. and Denoeux, T. and Destercke, S. and Seidenfeld, T.},

publisher = {SIPTA},

booktitle = {{ISIPTA} '13: Proceedings of the Eighth International Symposium on Imprecise Probability: Theories and Applications},

author = {Antonucci, A. and de Rosa, R. and Giusti, A. and Cuzzolin, F.},

pages = {13--22},

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url = {http://www.sipta.org/isipta13/proceedings/papers/s002.pdf}

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Downloadtitle = {Temporal data classification by imprecise dynamical models},

editor = {Cozman, F.G. and Denoeux, T. and Destercke, S. and Seidenfeld, T.},

publisher = {SIPTA},

booktitle = {{ISIPTA} '13: Proceedings of the Eighth International Symposium on Imprecise Probability: Theories and Applications},

author = {Antonucci, A. and de Rosa, R. and Giusti, A. and Cuzzolin, F.},

pages = {13--22},

year = {2013},

url = {http://www.sipta.org/isipta13/proceedings/papers/s002.pdf}

}

(2013). Nonlinear nonparametric mixed-effects models for unsupervised classification. *Computational Statistics* **28**(4), pp. 1549–1570.

@ARTICLE{azzimonti2013a,

title = {Nonlinear nonparametric mixed-effects models for unsupervised classification},

journal = {Computational Statistics},

volume = {28},

author = {Azzimonti, L. and Ieva, F. and Paganoni, A.M.},

number = {4},

pages = {1549--1570},

year = {2013},

doi = {10.1007/s00180-012-0366-5}

}

Downloadtitle = {Nonlinear nonparametric mixed-effects models for unsupervised classification},

journal = {Computational Statistics},

volume = {28},

author = {Azzimonti, L. and Ieva, F. and Paganoni, A.M.},

number = {4},

pages = {1549--1570},

year = {2013},

doi = {10.1007/s00180-012-0366-5}

}

(2013). Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks. In *Proceedings of the 27th AAAI Conference on Advances in Artificial Intelligence (AAAI)*, pp. 217–223.

@INPROCEEDINGS{decampos2013a,

title = {Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks},

booktitle = {Proceedings of the 27th {AAAI} Conference on Advances in Artificial Intelligence ({AAAI})},

author = {de Campos, C.P. and Cozman, F.G.},

pages = {217--223},

year = {2013}

}

Downloadtitle = {Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks},

booktitle = {Proceedings of the 27th {AAAI} Conference on Advances in Artificial Intelligence ({AAAI})},

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pages = {217--223},

year = {2013}

}

(2013). Discovering subgroups of patients from DNA copy number data using NMF on compacted matrices. *PLoS ONE* **8**(11), pp. e79720.

@ARTICLE{decampos2013d,

title = {Discovering subgroups of patients from {DNA} copy number data using {NMF} on compacted matrices},

journal = {{PLoS} {ONE}},

volume = {8},

author = {de Campos, C.P. and Rancoita, P.M.V. and Kwee, I. and Zucca, E. and Zaffalon, M. and Bertoni, F.},

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Downloadtitle = {Discovering subgroups of patients from {DNA} copy number data using {NMF} on compacted matrices},

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number = {11},

pages = {e79720},

year = {2013},

doi = {10.1371/journal.pone.0079720}

}

(2013). A Bayesian network model for predicting pregnancy after in vitro fertilization. *Computers in Biology and Medicine* **43**(11), pp. 1783–1792.

@ARTICLE{corani2013c,

title = {A {B}ayesian network model for predicting pregnancy after in vitro fertilization},

journal = {Computers in Biology and Medicine},

volume = {43},

author = {Corani, G. and Magli, M. and Giusti, A. and Gianaroli, L. and Gambardella, L.},

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number = {11},

pages = {1783--1792},

year = {2013},

doi = {10.1016/j.compbiomed.2013.07.035}

}

(2013). Credal model averaging of logistic regression for modeling the distribution of marmot burrows. In Cozman, F.G., Denoeux, T., Destercke, S., Seidenfeld, T. (Eds),, pp. 233–243.

@INPROCEEDINGS{corani2013a,

title = {Credal model averaging of logistic regression for modeling the distribution of marmot burrows},

journal = {Proceedings of {ISIPTA} '13 (the Eighth International Symposium on Imprecise Probability: Theories and Applications)},

editor = {Cozman, F.G. and Denoeux, T. and Destercke, S. and Seidenfeld, T. },

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author = {Corani, G. and Mignatti, A.},

pages = {233--243},

year = {2013}

}

(2013). Objective way to support embryo transfer: a probabilistic decision. *Human Reproduction* **28**(5), pp. 1210–1220.

@ARTICLE{corani2013d,

title = {Objective way to support embryo transfer: a probabilistic decision},

journal = {Human Reproduction},

volume = {28},

author = {Gianaroli, L. and Magli, M.C. and Gambardella, L. and Giusti, A. and Grugnetti, C. and Corani, G.},

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}

Downloadtitle = {Objective way to support embryo transfer: a probabilistic decision},

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pages = {1210--1220},

year = {2013},

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}

(2013). Prognostic impact of monocyte count at presentation in mantle cell lymphoma. *British Journal of Haematology* **162**(4), pp. 465–473.

@ARTICLE{decampos2013c,

title = {Prognostic impact of monocyte count at presentation in mantle cell lymphoma},

journal = {British Journal of Haematology},

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volume = {162},

author = {von Hohenstaufen, K.A. and Conconi, A. and de Campos, C.P. and Franceschetti, S. and Bertoni, F. and Margiotta Casaluci, G. and Stathis, A. and Ghielmini, M. and Stussi, G. and Cavalli, F. and Gaidano, G. and Zucca, E.},

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pages = {465--473},

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doi = {10.1111/bjh.12409},

url = {http://onlinelibrary.wiley.com/doi/10.1111/bjh.12409/pdf}

}

(2013). New prior near-ignorance models on the simplex. In Cozman, F.G., Denoeux, T., Destercke, S., Seidenfeld, T. (Eds), *ISIPTA '13: Proceedings of the Eighth International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, Compiegne (FR), pp. 1–9.

@INPROCEEDINGS{mangili2013a,

title = {New prior near-ignorance models on the simplex},

editor = {Cozman, F.G. and Denoeux, T. and Destercke, S. and Seidenfeld, T.},

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}

(2013). On the complexity of strong and epistemic credal networks. In *Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence*, AUAI Press, pp. 391–400.

@INPROCEEDINGS{maua2013a,

title = {On the complexity of strong and epistemic credal networks},

publisher = {AUAI Press},

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}

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pages = {391--400},

year = {2013}

}

(2013). On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables. *Artificial Intelligence* **205**, pp. 30–38.

@ARTICLE{maua2013b,

title = {On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables},

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volume = {205},

author = {Mau\'a, D.D. and de Campos, C.P. and Zaffalon, M.},

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doi = {10.1016/j.artint.2013.10.002}

}

Downloadtitle = {On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables},

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pages = {30--38},

year = {2013},

doi = {10.1016/j.artint.2013.10.002}

}

(2013). Conglomerable coherent lower previsions. In Kruse, R., Berthold, M. R., Moewes, C., Gil, M. A., Grzegorzewski, P., Hryniewicz, O. (Eds), *Synergies of Soft Computing and Statistics for Intelligent Data Analysis*, Advances in Intelligent and Soft Computing **190**, Springer Berlin Heidelberg, pp. 419–427.

@INCOLLECTION{zaffalon2012a,

title = {Conglomerable coherent lower previsions},

editor = {Kruse, R. and Berthold, M. R. and Moewes, C. and Gil, M. A. and Grzegorzewski, P. and Hryniewicz, O.},

publisher = {Springer Berlin Heidelberg},

series = {Advances in Intelligent and Soft Computing},

volume = {190},

booktitle = {Synergies of Soft Computing and Statistics for Intelligent Data Analysis},

author = {Miranda, E. and Zaffalon, M.},

pages = {419--427},

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}

Downloadtitle = {Conglomerable coherent lower previsions},

editor = {Kruse, R. and Berthold, M. R. and Moewes, C. and Gil, M. A. and Grzegorzewski, P. and Hryniewicz, O.},

publisher = {Springer Berlin Heidelberg},

series = {Advances in Intelligent and Soft Computing},

volume = {190},

booktitle = {Synergies of Soft Computing and Statistics for Intelligent Data Analysis},

author = {Miranda, E. and Zaffalon, M.},

pages = {419--427},

year = {2013},

doi = {10.1007/978-3-642-33042-1_45}

}

(2013). Conglomerable coherence. *International Journal of Approximate Reasoning* **54**(9), pp. 1322–1350.

@ARTICLE{zaffalon2013b,

title = {Conglomerable coherence},

journal = {International Journal of Approximate Reasoning},

volume = {54},

author = {Miranda, E. and Zaffalon, M.},

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pages = {1322--1350},

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year = {2013},

doi = {10.1016/j.ijar.2013.04.016}

}

(2013). Computing the conglomerable natural extension. In Cozman, F., Denoeux, T., Destercke, S., Seidenfeld, T. (Eds), *ISIPTA '13: Proceedings of the Eighth International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 255–264.

@INPROCEEDINGS{zaffalon2013c,

title = {Computing the conglomerable natural extension},

editor = {Cozman, F. and Denoeux, T. and Destercke, S. and Seidenfeld, T.},

publisher = {SIPTA},

booktitle = {{ISIPTA };'13: Proceedings of the Eighth International Symposium on Imprecise Probability: Theories and Applications},

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(2013). Probability and time. *Artificial Intelligence* **198**, pp. 1–51.

@ARTICLE{zaffalon2013a,

title = {Probability and time},

journal = {Artificial Intelligence},

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}

top## 2012

## An interval-valued dissimilarity measure for belief functions based on credal semantics

## Likelihood-based robust classification with Bayesian networks

## Active learning by the naive credal classifier

## Compression-based AODE classifiers

## Bayesian networks with imprecise probabilities: theory and application to classification

## A Bayesian network model for predicting the outcome of in vitro fertilization

## A prognostic model for multiple-embryo transfers

## Solving limited memory influence diagrams

## Updating credal networks is approximable in polynomial time

## Anytime marginal map inference

## The complexity of approximately solving influence diagrams

## Credal model averaging: dealing robustly with model uncertainty on small data sets

## Conglomerable natural extension

## Evaluating credal classifiers by utility-discounted predictive accuracy

(2012). An interval-valued dissimilarity measure for belief functions based on credal semantics. In Denoeux, T., Masson, M.H. (Eds), *Belief Functions: Theory and Applications*, Advances in Intelligent and Soft Computing **164**, Springer Berlin / Heidelberg, pp. 37–44.

@INPROCEEDINGS{antonucci2012a,

title = {An interval-valued dissimilarity measure for belief functions based on credal semantics},

editor = {Denoeux, T. and Masson, M.H.},

publisher = {Springer Berlin / Heidelberg},

series = {Advances in Intelligent and Soft Computing},

volume = {164},

booktitle = {Belief Functions: Theory and Applications},

author = {Antonucci, A.},

pages = {37--44},

year = {2012},

doi = {10.1007/978-3-642-29461-7_4}

}

Downloadtitle = {An interval-valued dissimilarity measure for belief functions based on credal semantics},

editor = {Denoeux, T. and Masson, M.H.},

publisher = {Springer Berlin / Heidelberg},

series = {Advances in Intelligent and Soft Computing},

volume = {164},

booktitle = {Belief Functions: Theory and Applications},

author = {Antonucci, A.},

pages = {37--44},

year = {2012},

doi = {10.1007/978-3-642-29461-7_4}

}

(2012). Likelihood-based robust classification with Bayesian networks. In *Communications in Computer and Information Science*, Advances in Computational Intelligence **299**(5), Springer Berlin / Heidelberg, pp. 491–500.

@INPROCEEDINGS{antonucci2012b,

title = {Likelihood-based robust classification with {B}ayesian networks},

publisher = {Springer Berlin / Heidelberg},

series = {Advances in Computational Intelligence},

volume = {299},

booktitle = {Communications in Computer and Information Science},

author = {Antonucci, A. and Cattaneo, M.E.V.G. and Corani, G.},

number = {5},

pages = {491--500},

year = {2012},

doi = {10.1007/978-3-642-31718-7_51}

}

Downloadtitle = {Likelihood-based robust classification with {B}ayesian networks},

publisher = {Springer Berlin / Heidelberg},

series = {Advances in Computational Intelligence},

volume = {299},

booktitle = {Communications in Computer and Information Science},

author = {Antonucci, A. and Cattaneo, M.E.V.G. and Corani, G.},

number = {5},

pages = {491--500},

year = {2012},

doi = {10.1007/978-3-642-31718-7_51}

}

(2012). Active learning by the naive credal classifier. In Cano, A., Gomez-Olmedo, M., Nielsen, T. (Eds), *Proc. of the 6th European Workshop on Probabilistic Graphical Models (PGM 2012)*, pp. 3–10.

@INPROCEEDINGS{antonucci2012c,

title = {Active learning by the naive credal classifier},

editor = {Cano, A. and Gomez-Olmedo, M. and Nielsen, T.},

booktitle = {Proc. {o}f the 6th European Workshop on Probabilistic Graphical Models ({PGM} 2012)},

author = {Antonucci, A. and Corani, G. and Gabaglio, S.},

pages = {3--10},

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Downloadtitle = {Active learning by the naive credal classifier},

editor = {Cano, A. and Gomez-Olmedo, M. and Nielsen, T.},

booktitle = {Proc. {o}f the 6th European Workshop on Probabilistic Graphical Models ({PGM} 2012)},

author = {Antonucci, A. and Corani, G. and Gabaglio, S.},

pages = {3--10},

year = {2012}

}

(2012). Compression-based AODE classifiers. In De Raedt, L. et al. (Ed), *Proc. 20th European Conference on Artificial Intelligence (ECAI 2012)*, pp. 264–269.

@INPROCEEDINGS{corani2012d,

title = {Compression-based {AODE} classifiers},

editor = {De Raedt, L. et al. },

booktitle = {Proc. 20th European Conference on Artificial Intelligence ({ECAI} 2012)},

author = {Corani, G. and Antonucci, A. and De Rosa, R.},

pages = {264--269},

year = {2012}

}

Downloadtitle = {Compression-based {AODE} classifiers},

editor = {De Raedt, L. et al. },

booktitle = {Proc. 20th European Conference on Artificial Intelligence ({ECAI} 2012)},

author = {Corani, G. and Antonucci, A. and De Rosa, R.},

pages = {264--269},

year = {2012}

}

(2012). Bayesian networks with imprecise probabilities: theory and application to classification. In Holmes, D.E., Jain, L.C. (Eds), *Data Mining: Foundations and Intelligent Paradigms*, Intelligent Systems Reference Library **23**, Springer, Berlin / Heidelberg, pp. 49–93.

@INCOLLECTION{corani2012c,

title = {Bayesian networks with imprecise probabilities: theory and application to classification},

editor = {Holmes, D.E. and Jain, L.C.},

publisher = {Springer, Berlin / Heidelberg},

series = {Intelligent Systems Reference Library},

volume = {23},

booktitle = {Data Mining: Foundations and Intelligent Paradigms},

author = {Corani, G. and Antonucci, A. and Zaffalon, M.},

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year = {2012},

doi = {10.1007/978-3-642-23166-7_4}

}

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editor = {Holmes, D.E. and Jain, L.C.},

publisher = {Springer, Berlin / Heidelberg},

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volume = {23},

booktitle = {Data Mining: Foundations and Intelligent Paradigms},

author = {Corani, G. and Antonucci, A. and Zaffalon, M.},

pages = {49--93},

year = {2012},

doi = {10.1007/978-3-642-23166-7_4}

}

(2012). A Bayesian network model for predicting the outcome of in vitro fertilization. In Cano, A., Gomez-Olmedo, M., Nielsen, T. (Eds), *Proc. of the 6th European Workshop on Probabilistic Graphical Models (PGM 2012)*, pp. 75–82.

@INPROCEEDINGS{corani2012e,

title = {A {B}ayesian network model for predicting the outcome of in vitro fertilization},

editor = {Cano, A. and Gomez-Olmedo, M. and Nielsen, T.},

booktitle = {Proc. {o}f the 6th European Workshop on Probabilistic Graphical Models ({PGM} 2012)},

author = {Corani, G. and Magli, C. and Giusti, A. and Gianaroli, L. and Gambardella, L.},

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Downloadtitle = {A {B}ayesian network model for predicting the outcome of in vitro fertilization},

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pages = {75--82},

year = {2012}

}

(2012). A prognostic model for multiple-embryo transfers. *Human Reproduction (Supplement: Abstract book, Proc. Annual Meeting ESHRE 2012)* **27**(2), pp. ii162–ii205.

@ARTICLE{corani2012b,

title = {A prognostic model for multiple-embryo transfers},

journal = {Human Reproduction (Supplement: Abstract {b}ook, Proc. Annual Meeting {ESHRE} 2012)},

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author = {Magli, C. and Corani, G. and Giusti, A. and Castelletti, E. and Gambardella, L. and Gianaroli, L.},

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journal = {Human Reproduction (Supplement: Abstract {b}ook, Proc. Annual Meeting {ESHRE} 2012)},

volume = {27},

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number = {2},

pages = {ii162--ii205},

year = {2012},

doi = {10.1093/humrep/27.s2.77}

}

(2012). Solving limited memory influence diagrams. *Journal of Artificial Intelligence Research* **44**, pp. 97–140.

@ARTICLE{maua2012a,

title = {Solving limited memory influence diagrams},

journal = {Journal of Artificial Intelligence Research},

volume = {44},

author = {Mau\'a, D.D. and de Campos, C.P. and Zaffalon, M.},

pages = {97--140},

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url = {http://www.jair.org/media/3625/live-3625-6282-jair.pdf}

}

Downloadtitle = {Solving limited memory influence diagrams},

journal = {Journal of Artificial Intelligence Research},

volume = {44},

author = {Mau\'a, D.D. and de Campos, C.P. and Zaffalon, M.},

pages = {97--140},

year = {2012},

url = {http://www.jair.org/media/3625/live-3625-6282-jair.pdf}

}

(2012). Updating credal networks is approximable in polynomial time. *International Journal of Approximate Reasoning* **53**(8), pp. 1183–1199.

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url = {http://www.sciencedirect.com/science/article/pii/S0888613X12000904?v=s5}

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(2012). Anytime marginal map inference. In *Proceedings of the 28th International Conference on Machine Learning (ICML 2012)*, pp. 1471–1478.

@INPROCEEDINGS{maua2012b,

title = {Anytime marginal map inference},

booktitle = {Proceedings of the 28th International Conference on Machine Learning ({ICML} 2012)},

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year = {2012},

url = {http://icml.cc/2012/papers/728.pdf}

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(2012). The complexity of approximately solving influence diagrams. In *Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI 2012)*, pp. 604–613.

@INPROCEEDINGS{maua2012c,

title = {The complexity of approximately solving influence diagrams},

booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence ({UAI} 2012)},

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Downloadtitle = {The complexity of approximately solving influence diagrams},

booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence ({UAI} 2012)},

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pages = {604--613},

year = {2012},

url = {http://www.auai.org/uai2012/papers/166.pdf}

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(2012). Credal model averaging: dealing robustly with model uncertainty on small data sets. In *Proc. 6th International Congress on Environmental Modelling and Software (iEMSs 2012)*, pp. 163–170.

@INCOLLECTION{corani2012a,

title = {Credal model averaging: dealing robustly with model uncertainty on small data sets},

booktitle = {Proc. 6th International Congress on Environmental Modelling and Software ({iEMSs} 2012)},

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pages = {163-170},

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(2012). Conglomerable natural extension. *International Journal of Approximate Reasoning* **53**(8), pp. 1200–1227.

@ARTICLE{zaffalon2012b,

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(2012). Evaluating credal classifiers by utility-discounted predictive accuracy. *International Journal of Approximate Reasoning* **53**(8), pp. 1282–1301.

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number = {8},

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year = {2012},

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}

top## 2011

## The imprecise noisy-or gate

## Decision making by credal nets

## Likelihood-based naive credal classifier

## Action recognition by imprecise hidden Markov models

## New complexity results for MAP in Bayesian networks

## Inference with multinomial data: why to weaken the prior strength

## Efficient structure learning of Bayesian networks using constraints

## Bayesian networks and the imprecise Dirichlet model applied to recognition problems

## Independent natural extension

## Invariant Natural Killer T-cell reconstitution in pediatric leukemia patients given HLA-haploidentical stem cell transplantation defines distinct CD4+ and CD4- subset dynamics and associates with the remission state

## Solving decision problems with limited information

## A fully polynomial time approximation scheme for updating credal networks of bounded treewidth and number of variable states

## Conglomerable natural extension

## Genome-wide DNA profiling of marginal zone lymphomas identifies subtype-specific lesions with an impact on the clinical outcome

## Utility-based accuracy measures to empirically evaluate credal classifiers

(2011). The imprecise noisy-or gate. In *FUSION 2011: Proceedings of the 14th International Conference on Information Fusion*, IEEE, pp. 709–715.

@INPROCEEDINGS{antonucci2011c,

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year = {2011}

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Downloadtitle = {The imprecise noisy-or gate},

publisher = {IEEE},

booktitle = {{FUSION} 2011: Proceedings of the 14th International Conference on Information Fusion},

author = {Antonucci, A.},

pages = {709--715},

year = {2011}

}

(2011). Decision making by credal nets. In *Proceedings of the International Conference on Intelligent Human-machine Systems and Cybernetics (IHMSC 2011)* **1**, IEEE, Hangzhou (China), pp. 201–204.

@INPROCEEDINGS{antonucci2011d,

title = {Decision making by credal nets},

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volume = {1},

booktitle = {Proceedings of the International Conference on Intelligent Human-{m}achine Systems and Cybernetics ({IHMSC} 2011)},

author = {Antonucci, A. and de Campos, C.P.},

pages = {201--204},

year = {2011}

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publisher = {IEEE},

address = {Hangzhou (China)},

volume = {1},

booktitle = {Proceedings of the International Conference on Intelligent Human-{m}achine Systems and Cybernetics ({IHMSC} 2011)},

author = {Antonucci, A. and de Campos, C.P.},

pages = {201--204},

year = {2011}

}

(2011). Likelihood-based naive credal classifier. In *ISIPTA '11: Proceedings of the Seventh International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 21–30.

@INPROCEEDINGS{antonucci2011a,

title = {Likelihood-based naive credal classifier},

publisher = {SIPTA},

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publisher = {SIPTA},

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pages = {21--30},

year = {2011},

url = {http://www.sipta.org/isipta11/proceedings/papers/s032.pdf}

}

(2011). Action recognition by imprecise hidden Markov models. In *Proceedings of the 2011 International Conference on Image Processing, Computer Vision and Pattern Recognition, IPCV 2011*, CSREA Press, pp. 474–478.

@INPROCEEDINGS{antonucci2011b,

title = {Action recognition by imprecise hidden {M}arkov models},

publisher = {CSREA Press},

booktitle = {Proceedings of the 2011 International Conference on Image Processing, Computer Vision and Pattern Recognition, {IPCV} 2011},

author = {Antonucci, A. and de Rosa, R. and Giusti, A.},

pages = {474--478},

year = {2011},

url = {http://www.lidi.info.unlp.edu.ar/WorldComp2011-Mirror/IPC5150.pdf}

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Downloadtitle = {Action recognition by imprecise hidden {M}arkov models},

publisher = {CSREA Press},

booktitle = {Proceedings of the 2011 International Conference on Image Processing, Computer Vision and Pattern Recognition, {IPCV} 2011},

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pages = {474--478},

year = {2011},

url = {http://www.lidi.info.unlp.edu.ar/WorldComp2011-Mirror/IPC5150.pdf}

}

(2011). New complexity results for MAP in Bayesian networks. In *International Joint Conference on Artificial Intelligence (IJCAI)*, AAAI Press, pp. 2100–2106.

@INPROCEEDINGS{decampos2011c,

title = {New complexity results for {MAP} in {B}ayesian networks},

publisher = {AAAI Press},

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(2011). Inference with multinomial data: why to weaken the prior strength. In *International Joint Conference on Artificial Intelligence (IJCAI)*, AAAI Press, pp. 2107–2112.

@INPROCEEDINGS{decampos2011e,

title = {Inference with multinomial data: why to weaken the prior strength},

publisher = {AAAI Press},

booktitle = {International Joint Conference on Artificial Intelligence ({IJCAI})},

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Downloadtitle = {Inference with multinomial data: why to weaken the prior strength},

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pages = {2107--2112},

year = {2011},

url = {http://ijcai.org/papers11/Papers/IJCAI11-352.pdf}

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(2011). Efficient structure learning of Bayesian networks using constraints. *Journal of Machine Learning Research* **12**, pp. 663–689.

@ARTICLE{decampos2011a,

title = {Efficient structure learning of {B}ayesian networks using constraints},

journal = {Journal of Machine Learning Research},

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year = {2011},

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(2011). Bayesian networks and the imprecise Dirichlet model applied to recognition problems. In Liu, W. (Ed), *Symbolic and Quantitative Approaches to Reasoning With Uncertainty*, Lecture Notes in Computer Science **6717**, Springer, Berlin / Heidelberg, pp. 158–169.

@INPROCEEDINGS{decampos2011f,

title = {Bayesian networks and the imprecise {D}irichlet model applied to recognition problems},

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(2011). Independent natural extension. *Artificial Intelligence* **175**, pp. 1911–1950.

@ARTICLE{zaffalon2011a,

title = {Independent natural extension},

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Downloadtitle = {Independent natural extension},

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(2011). Invariant Natural Killer T-cell reconstitution in pediatric leukemia patients given HLA-haploidentical stem cell transplantation defines distinct CD4+ and CD4- subset dynamics and associates with the remission state. *The Journal of Immunology* **186**(7), pp. 4490–4499.

@ARTICLE{azzimonti2011a,

title = {Invariant {N}atural {K}iller {T}-cell reconstitution in pediatric leukemia patients given {HLA}-haploidentical stem cell transplantation defines distinct {CD4+} and {CD4}- subset dynamics and associates with the remission state},

journal = {The Journal of Immunology},

volume = {186},

author = {de Lalla, C. and Rinaldi, A. and Montagna, D. and Azzimonti, L. and Bernardo, M.E. and Sangalli, L.M. and Paganoni, A.M. and Maccario, R. and Cesare-Merlone, A.D. and Zecca, M. and Locatelli, F. and Dellabona, P. and Casorati, G.},

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volume = {186},

author = {de Lalla, C. and Rinaldi, A. and Montagna, D. and Azzimonti, L. and Bernardo, M.E. and Sangalli, L.M. and Paganoni, A.M. and Maccario, R. and Cesare-Merlone, A.D. and Zecca, M. and Locatelli, F. and Dellabona, P. and Casorati, G.},

number = {7},

pages = {4490--4499},

year = {2011},

doi = {10.4049/jimmunol.1003748}

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(2011). Solving decision problems with limited information. In Shawe-Taylor, J., Zemel, R.S., Bartlett, P., Pereira, F.C.N., Weinberger, K.Q. (Eds), *Advances in Neural Information Processing Systems 24 (NIPS 2011)*, pp. 603–611.

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(2011). A fully polynomial time approximation scheme for updating credal networks of bounded treewidth and number of variable states. In Coolen, F., de Cooman, G., Fetz, T., Oberguggenberger, M. (Eds), *ISIPTA '11: Proceedings of the Seventh International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, Innsbruck, Austria, pp. 277–286.

@INPROCEEDINGS{maua2011b,

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pages = {277--286},

year = {2011},

url = {http://leo.ugr.es/sipta/isipta11/proceedings/papers/s035.pdf}

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(2011). Conglomerable natural extension. In Coolen, F., de Cooman, G., Fetz, T., Oberguggenberger, M. (Eds), *ISIPTA '11: Proceedings of the Seventh International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 287–296.

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(2011). Genome-wide DNA profiling of marginal zone lymphomas identifies subtype-specific lesions with an impact on the clinical outcome. *Blood* **117**(5), pp. 1595–1604.

@ARTICLE{decampos2011b,

title = {Genome-wide {DNA} profiling of marginal zone lymphomas identifies subtype-specific lesions with an impact on the clinical outcome},

journal = {Blood},

publisher = {The American Society of Hematology},

volume = {117},

author = {Rinaldi, A. and Mian, M. and Chigrinova, E. and Arcaini, L. and Bhagat, G. and Novak, U. and Rancoita, P.M.V. and Campos, C.P.D. and Forconi, F. and Gascoyne, R.D. and Facchetti, F. and Ponzoni, M. and Govi, S. and Ferreri, A.J.M. and Mollejo, M. and Piris, M.A. and Baldini, L. and Soulier, J. and Thieblemont, C. and Canzonieri, V. and Gattei, V. and Marasca, R. and Franceschetti, S. and Gaidano, G. and Tucci, A. and Uccella, S. and Tibiletti, M.G. and Dirnhofer, S. and Tripodo, C. and Doglioni, C. and Favera, R.D. and Cavalli, F. and Zucca, E. and Kwee, I. and Bertoni, F.},

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Downloadtitle = {Genome-wide {DNA} profiling of marginal zone lymphomas identifies subtype-specific lesions with an impact on the clinical outcome},

journal = {Blood},

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number = {5},

pages = {1595--1604},

year = {2011},

doi = {10.1182/blood-2010-01-264275},

url = {http://bloodjournal.hematologylibrary.org/content/117/5/1595.full.pdf+html}

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(2011). Utility-based accuracy measures to empirically evaluate credal classifiers. In Coolen, F., de Cooman, G., Fetz, T., Oberguggenberger, M. (Eds), *ISIPTA '11: Proceedings of the Seventh International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 401–410.

@INPROCEEDINGS{zaffalon2011b,

title = {Utility-based accuracy measures to empirically evaluate credal classifiers},

editor = {Coolen, F. and de Cooman, G. and Fetz, T. and Oberguggenberger, M.},

publisher = {SIPTA},

booktitle = {{ISIPTA} '11: Proceedings of the Seventh International Symposium on Imprecise Probability: Theories and Applications},

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editor = {Coolen, F. and de Cooman, G. and Fetz, T. and Oberguggenberger, M.},

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pages = {401--410},

year = {2011},

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top## 2010

## Credal sets approximation by lower probabilities: application to credal networks

## Generalized loopy 2U: a new algorithm for approximate inference in credal networks

## Properties of Bayesian Dirichlet scores to learn Bayesian network structures

## An improved structural EM to learn dynamic Bayesian nets

## Epistemic irrelevance in credal nets: the case of imprecise markov trees

## Factorisation properties of the strong product

## Independent natural extension

## Restricting the IDM for classification

## A tree augmented classifier based on extreme imprecise Dirichlet model

## Robust texture recognition using credal classifiers

## Notes on desirability and conditional lower previsions

## Conditional models: coherence and inference through sequences of joint mass functions

## Inference and risk measurement with the pari-mutuel model

## Building knowledge-based expert systems by credal networks: a tutorial

## Genomic lesions associated with a different clinical outcome in diffuse large B-Cell lymphoma treated with R-CHOP-21

(2010). Credal sets approximation by lower probabilities: application to credal networks. In Hüllermeier, E., Kruse, R., Hoffmann, F. (Eds), *Computational Intelligence for Knowledge-based Systems Design, 13th International Conference on Information Processing and Management of Uncertainty, IPMU 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings*, Lecture Notes in Computer Science **6178**, Springer, pp. 716–725.

@INPROCEEDINGS{antonucci2010a,

title = {Credal sets approximation by lower probabilities: application to credal networks},

editor = {H\"ullermeier, E. and Kruse, R. and Hoffmann, F.},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

volume = {6178},

booktitle = {Computational Intelligence for Knowledge-{b}ased Systems Design, 13th International Conference on Information Processing and Management of Uncertainty, {IPMU} 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings},

author = {Antonucci, A. and Cuzzolin, F.},

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editor = {H\"ullermeier, E. and Kruse, R. and Hoffmann, F.},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

volume = {6178},

booktitle = {Computational Intelligence for Knowledge-{b}ased Systems Design, 13th International Conference on Information Processing and Management of Uncertainty, {IPMU} 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings},

author = {Antonucci, A. and Cuzzolin, F.},

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(2010). Generalized loopy 2U: a new algorithm for approximate inference in credal networks. *International Journal of Approximate Reasoning* **55**(5), pp. 474–484.

@ARTICLE{antonucci2010c,

title = {Generalized loopy {2U}: a new algorithm for approximate inference in credal networks},

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doi = {10.1016/j.ijar.2010.01.007}

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number = {5},

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(2010). Properties of Bayesian Dirichlet scores to learn Bayesian network structures. In *AAAI Conference on Artificial Intelligence*, AAAI Press, pp. 431–436.

@INPROCEEDINGS{decampos2010c,

title = {Properties of {B}ayesian {D}irichlet scores to learn {B}ayesian network structures},

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year = {2010},

url = {http://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/1704/2013}

}

(2010). An improved structural EM to learn dynamic Bayesian nets. In *20th International Conference on Pattern Recognition (ICPR)*, pp. 601–604.

@INPROCEEDINGS{decampos2010d,

title = {An improved structural {EM} to learn dynamic {B}ayesian nets},

booktitle = {20th International Conference on Pattern Recognition ({ICPR})},

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year = {2010},

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}

(2010). Epistemic irrelevance in credal nets: the case of imprecise markov trees. *International Journal of Approximate Reasoning* **51**(9), pp. 1029–1052.

@ARTICLE{antonucci2010b,

title = {Epistemic irrelevance in credal nets: the case of imprecise markov trees},

journal = {International Journal of Approximate Reasoning},

volume = {51},

author = {de Cooman, G. and Hermans, F. and Antonucci, A. and Zaffalon, M.},

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number = {9},

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year = {2010},

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(2010). Factorisation properties of the strong product. In Borgelt, C., González Rodrìguez, G., Trutschnig, W., Asunción Lubiano, M., Gil, M.A., Grzegorzewski, P., Hryniewicz, O. (Eds), *Combining Soft Computing and Statistical Methods in Data Analysis*, Advances in Intelligent and Soft Computing **77**, Springer, pp. 139–147.

@INPROCEEDINGS{zaffalon2010c,

title = {Factorisation properties of the strong product},

editor = {Borgelt, C. and Gonz\'alez Rodr\`iguez, G. and Trutschnig, W. and Asunci\'on Lubiano, M. and Gil, M.A. and Grzegorzewski, P. and Hryniewicz, O.},

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year = {2010},

doi = {10.1007/978-3-642-14746-3_18}

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(2010). Independent natural extension. In Hüllermeier, E., Kruse, R., Hoffmann, F. (Eds), *Computational Intelligence for Knowledge-based Systems Design*, Lecture Notes in Computer Science **6178**, Springer, pp. 737–746.

@INPROCEEDINGS{zaffalon2010b,

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editor = {H\"ullermeier, E. and Kruse, R. and Hoffmann, F.},

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pages = {737--746},

year = {2010},

doi = {10.1007/978-3-642-14049-5_75}

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(2010). Restricting the IDM for classification. In Hullermeier, E., Kruse, R., Hoffmann, F. (Eds), *Information Processing and Management of Uncertainty in Knowledge-based Systems. Theory and Methods*, Communications in Computer and Information Science **80**, Springer, Berlin / Heidelberg, pp. 328–337.

@INCOLLECTION{corani2010a,

title = {Restricting the {IDM} for classification},

editor = {Hullermeier, E. and Kruse, R. and Hoffmann, F.},

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(2010). A tree augmented classifier based on extreme imprecise Dirichlet model. *International Journal of Approximate Reasoning* **51**(9), pp. 1053–1068.

@ARTICLE{Corani2010b,

title = {A tree augmented classifier based on extreme imprecise {D}irichlet model},

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(2010). Robust texture recognition using credal classifiers. In Labrosse, F., Zwiggelaar, R., Liu, Y., Tiddeman, B. (Eds), *Proceedings of the British Machine Vision Conference*, BMVA Press, pp. 78.1–78.10.

@INPROCEEDINGS{corani2010c,

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(2010). Notes on desirability and conditional lower previsions. *Annals of Mathematics and Artificial Intelligence* **60**(3–4), pp. 251–309.

@ARTICLE{zaffalon2010e,

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}

(2010). Conditional models: coherence and inference through sequences of joint mass functions. *Journal of Statistical Planning and Inference* **140**(7), pp. 1805–1833.

@ARTICLE{zaffalon2010a,

title = {Conditional models: coherence and inference through sequences of joint mass functions},

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year = {2010},

doi = {10.1016/j.jspi.2010.01.005}

}

(2010). Inference and risk measurement with the pari-mutuel model. *International Journal of Approximate Reasoning* **51**(9), pp. 1145–1158.

@ARTICLE{zaffalon2010d,

title = {Inference and risk measurement with the pari-mutuel model},

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}

(2010). Building knowledge-based expert systems by credal networks: a tutorial. In Baswell, A.R. (Ed), *Advances in Mathematics Research* **11**, Nova Science Publishers, New York.

@INBOOK{antonucci2010d,

title = {Building knowledge-based expert systems by credal networks: a tutorial},

editor = {Baswell, A.R.},

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(2010). Genomic lesions associated with a different clinical outcome in diffuse large B-Cell lymphoma treated with R-CHOP-21. *British Journal of Haematology* **151**(3), pp. 221–231.

@ARTICLE{decampos2010a,

title = {Genomic lesions associated with a different clinical outcome in diffuse large {B}-{C}ell lymphoma treated with {R}-{CHOP}-21},

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publisher = {Blackwell Publishing Ltd},

volume = {151},

author = {Scandurra, M. and Mian, M. and Greiner, T.C. and Rancoita, P.M.V. and De Campos, C.P. and Chan, W.C. and Vose, J.M. and Chigrinova, E. and Inghirami, G. and Chiappella, A. and Baldini, L. and Ponzoni, M. and Ferreri, A.J.M. and Franceschetti, S. and Gaidano, G. and Montes-Moreno, S. and Piris, M.A. and Facchetti, F. and Tucci, A. and Nomdedeu, J.F. and Lazure, T. and Lambotte, O. and Uccella, S. and Pinotti, G. and Pruneri, G. and Martinelli, G. and Young, K.H. and Tibiletti, M.G. and Rinaldi, A. and Zucca, E. and Kwee, I. and Bertoni, F.},

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author = {Scandurra, M. and Mian, M. and Greiner, T.C. and Rancoita, P.M.V. and De Campos, C.P. and Chan, W.C. and Vose, J.M. and Chigrinova, E. and Inghirami, G. and Chiappella, A. and Baldini, L. and Ponzoni, M. and Ferreri, A.J.M. and Franceschetti, S. and Gaidano, G. and Montes-Moreno, S. and Piris, M.A. and Facchetti, F. and Tucci, A. and Nomdedeu, J.F. and Lazure, T. and Lambotte, O. and Uccella, S. and Pinotti, G. and Pruneri, G. and Martinelli, G. and Young, K.H. and Tibiletti, M.G. and Rinaldi, A. and Zucca, E. and Kwee, I. and Bertoni, F.},

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url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2141.2010.08326.x/pdf}

}

top## 2009

## Multiple model tracking by imprecise Markov trees

## Credal networks for military identification problems

## Modeling unreliable observations in Bayesian networks by credal networks

## Assembling a consistent set of sentences in relational probabilistic logic with stochastic independence

## Structure learning of Bayesian networks using constraints

## Semi-qualitative probabilistic networks in computer vision problems

## Semi-qualitative probabilistic networks in computer vision problems

## Epistemic irrelevance in credal networks: the case of imprecise Markov trees

## A tree augmented classifier based on extreme imprecise Dirichlet model

## Reproducing human decisions in reservoir management: the case of lake lugano

## Lazy naive credal classifier

## Coherence graphs

## Natural extension as a limit of regular extensions

## The pari-mutuel model

## Limits of learning about a categorical latent variable under prior near-ignorance

## Conservative inference rule for uncertain reasoning under incompleteness

(2009). Multiple model tracking by imprecise Markov trees. In *FUSION 2009: Proceedings of the 12th International Conference on Information Fusion*, IEEE.

@INPROCEEDINGS{antonucci2009e,

title = {Multiple model tracking by imprecise {M}arkov trees},

publisher = {IEEE},

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author = {Antonucci, A. and Benavoli, A. and Zaffalon, M. and de Cooman, G. and Hermans, F.},

year = {2009},

url = {http://isif.org/fusion/proceedings/fusion09CD/data/papers/0478.pdf}

}

Downloadtitle = {Multiple model tracking by imprecise {M}arkov trees},

publisher = {IEEE},

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author = {Antonucci, A. and Benavoli, A. and Zaffalon, M. and de Cooman, G. and Hermans, F.},

year = {2009},

url = {http://isif.org/fusion/proceedings/fusion09CD/data/papers/0478.pdf}

}

(2009). Credal networks for military identification problems. *International Journal of Approximate Reasoning* **50**(2), pp. 666–679.

@ARTICLE{antonucci2009a,

title = {Credal networks for military identification problems},

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number = {2},

pages = {666--679},

year = {2009},

doi = {10.1016/j.ijar.2009.01.005}

}

Downloadtitle = {Credal networks for military identification problems},

journal = {International Journal of Approximate Reasoning},

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(2009). Modeling unreliable observations in Bayesian networks by credal networks. In Godo, L., Pugliese, A. (Eds), *Scalable Uncertainty Management, Third International Conference, SUM 2009, Washington, DC, USA, September 28–30, 2009. Proceedings*, Lecture Notes in Computer Science **5785**, Springer, pp. 28–39.

@INPROCEEDINGS{antonucci2009g,

title = {Modeling unreliable observations in {B}ayesian networks by credal networks},

editor = {Godo, L. and Pugliese, A.},

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volume = {5785},

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year = {2009},

doi = {10.1007/978-3-642-04388-8_4}

}

(2009). Assembling a consistent set of sentences in relational probabilistic logic with stochastic independence. *Journal of Applied Logic* **7**(2), pp. 137–154.

@ARTICLE{decampos2009d,

title = {Assembling a consistent set of sentences in relational probabilistic logic with stochastic independence},

journal = {Journal of Applied Logic},

volume = {7},

author = {de Campos, C.P. and Cozman, F.G. and Luna, J.E.O.},

number = {2},

pages = {137--154},

year = {2009},

doi = {10.1016/j.jal.2007.11.002}

}

Downloadtitle = {Assembling a consistent set of sentences in relational probabilistic logic with stochastic independence},

journal = {Journal of Applied Logic},

volume = {7},

author = {de Campos, C.P. and Cozman, F.G. and Luna, J.E.O.},

number = {2},

pages = {137--154},

year = {2009},

doi = {10.1016/j.jal.2007.11.002}

}

(2009). Structure learning of Bayesian networks using constraints. In *International Conference on Machine Learning (ICML)* **382**, ACM, pp. 113–120.

@INPROCEEDINGS{decampos2009e,

title = {Structure learning of {B}ayesian networks using constraints},

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pages = {113--120},

year = {2009},

doi = {10.1145/1553374.1553389}

}

(2009). Semi-qualitative probabilistic networks in computer vision problems. *Journal of Statistical Theory and Practice* **3**(1), pp. 197–210.

@ARTICLE{decampos2009c,

title = {Semi-qualitative probabilistic networks in computer vision problems},

journal = {Journal of Statistical Theory and Practice},

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number = {1},

pages = {197--210},

year = {2009},

doi = {10.1080/15598608.2009.10411920}

}

Downloadtitle = {Semi-qualitative probabilistic networks in computer vision problems},

journal = {Journal of Statistical Theory and Practice},

publisher = {Grace Scientific Publishing LLC},

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number = {1},

pages = {197--210},

year = {2009},

doi = {10.1080/15598608.2009.10411920}

}

(2009). Semi-qualitative probabilistic networks in computer vision problems. In Coolen-Schrijner, P., Coolen, F., Troffaes, M.C.M., Augustin, T. (Eds), *Imprecision in Statistical Theory and Practice.*, Grace Scientific Publishing LLC, Greensboro, North-Carolina, USA, pp. 207–220.

@INBOOK{decampos2009a,

title = {Semi-qualitative probabilistic networks in computer vision problems},

editor = {Coolen-Schrijner, P. and Coolen, F. and Troffaes, M.C.M. and Augustin, T.},

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Downloadtitle = {Semi-qualitative probabilistic networks in computer vision problems},

editor = {Coolen-Schrijner, P. and Coolen, F. and Troffaes, M.C.M. and Augustin, T.},

publisher = {Grace Scientific Publishing LLC},

address = {Greensboro, North-Carolina, USA},

booktitle = {Imprecision in Statistical Theory and Practice.},

author = {de Campos, C.P. and Zhang, L. and Tong, Y. and Ji, Q.},

pages = {207--220},

year = {2009},

url = {http://www.amazon.com/Imprecision-Statistical-Practice-Pauline-Coolen-Schrijner/dp/0982399804}

}

(2009). Epistemic irrelevance in credal networks: the case of imprecise Markov trees. In Augustin, T., Coolen, F., Moral, S., Troffaes, M.C.M. (Eds), *ISIPTA '09: Proceedings of the Sixth International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 149–158.

@INPROCEEDINGS{antonucci2009c,

title = {Epistemic irrelevance in credal networks: the case of imprecise {M}arkov trees},

editor = {Augustin, T. and Coolen, F. and Moral, S. and Troffaes, M.C.M.},

publisher = {SIPTA},

booktitle = {{ISIPTA} '09: Proceedings of the Sixth International Symposium on Imprecise Probability: Theories and Applications},

author = {de Cooman, G. and Hermans, F. and Antonucci, A. and Zaffalon, M.},

pages = {149--158},

year = {2009},

url = {http://www.sipta.org/isipta09/proceedings/papers/s053.pdf}

}

Downloadtitle = {Epistemic irrelevance in credal networks: the case of imprecise {M}arkov trees},

editor = {Augustin, T. and Coolen, F. and Moral, S. and Troffaes, M.C.M.},

publisher = {SIPTA},

booktitle = {{ISIPTA} '09: Proceedings of the Sixth International Symposium on Imprecise Probability: Theories and Applications},

author = {de Cooman, G. and Hermans, F. and Antonucci, A. and Zaffalon, M.},

pages = {149--158},

year = {2009},

url = {http://www.sipta.org/isipta09/proceedings/papers/s053.pdf}

}

(2009). A tree augmented classifier based on extreme imprecise Dirichlet model. In Augustin, T., Coolen, F.P.A., Moral, S., Troffaes, M.C.M. (Eds), *ISIPTA '09: Proceedings of the Sixth International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, Durham, UK, pp. 89–98.

@INPROCEEDINGS{corani2009c,

title = {A tree augmented classifier based on extreme imprecise {D}irichlet model},

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publisher = {SIPTA},

address = {Durham, UK},

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Downloadtitle = {A tree augmented classifier based on extreme imprecise {D}irichlet model},

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pages = {89--98},

year = {2009},

url = {http://www.sipta.org/isipta09/proceedings/papers/s060.pdf}

}

(2009). Reproducing human decisions in reservoir management: the case of lake lugano. In *Information Technologies in Environmental Engineering*, Springer, Berlin / Heidelberg, pp. 252–263.

@INCOLLECTION{corani2009a,

title = {Reproducing human decisions in reservoir management: the case of lake lugano},

publisher = {Springer, Berlin / Heidelberg},

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Downloadtitle = {Reproducing human decisions in reservoir management: the case of lake lugano},

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pages = {252--263},

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doi = {10.1007/978-3-540-88351-7_19}

}

(2009). Lazy naive credal classifier. In *Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery From Uncertain Data*, U '09, ACM, New York, NY, USA, pp. 30–37.

@INPROCEEDINGS{corani2009b,

title = {Lazy naive credal classifier},

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(2009). Coherence graphs. *Artificial Intelligence* **173**, pp. 104–144.

@ARTICLE{zaffalon2009b,

title = {Coherence graphs},

journal = {Artificial Intelligence},

volume = {173},

author = {Miranda, E. and Zaffalon, M.},

pages = {104--144},

year = {2009},

doi = {10.1016/j.artint.2008.09.001}

}

Downloadtitle = {Coherence graphs},

journal = {Artificial Intelligence},

volume = {173},

author = {Miranda, E. and Zaffalon, M.},

pages = {104--144},

year = {2009},

doi = {10.1016/j.artint.2008.09.001}

}

(2009). Natural extension as a limit of regular extensions. In Augustin, T., Coolen, F., Troffaes, M.C.M., Moral, S. (Eds), *ISIPTA '09: Proceedings of the Sixth International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 327–336.

@INPROCEEDINGS{zaffalon2009e,

title = {Natural extension as a limit of regular extensions},

editor = {Augustin, T. and Coolen, F. and Troffaes, M.C.M. and Moral, S.},

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booktitle = {{ISIPTA} '09: Proceedings of the Sixth International Symposium on Imprecise Probability: Theories and Applications},

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editor = {Augustin, T. and Coolen, F. and Troffaes, M.C.M. and Moral, S.},

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pages = {327--336},

year = {2009},

url = {http://www.sipta.org/isipta09/proceedings/papers/s012.pdf}

}

(2009). The pari-mutuel model. In Augustin, T., Coolen, F., Troffaes, M.C.M., Moral, S. (Eds), *ISIPTA '09: Proceedings of the Sixth International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 347–356.

@INPROCEEDINGS{zaffalon2009d,

title = {The pari-mutuel model},

editor = {Augustin, T. and Coolen, F. and Troffaes, M.C.M. and Moral, S.},

publisher = {SIPTA},

booktitle = {{ISIPTA} '09: Proceedings of the Sixth International Symposium on Imprecise Probability: Theories and Applications},

author = {Pelessoni, R. and Vicig, P. and Zaffalon, M.},

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pages = {347--356},

year = {2009},

url = {http://www.sipta.org/isipta09/proceedings/papers/s028.pdf}

}

(2009). Limits of learning about a categorical latent variable under prior near-ignorance. *International Journal of Approximate Reasoning* **50**, pp. 597–611.

@ARTICLE{zaffalon2009a,

title = {Limits of learning about a categorical latent variable under prior near-ignorance},

journal = {International Journal of Approximate Reasoning},

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pages = {597--611},

year = {2009},

doi = {10.1016/j.ijar.2008.08.003}

}

(2009). Conservative inference rule for uncertain reasoning under incompleteness. *Journal of Artificial Intelligence Research* **34**, pp. 757–821.

@ARTICLE{zaffalon2009c,

title = {Conservative inference rule for uncertain reasoning under incompleteness},

journal = {Journal of Artificial Intelligence Research},

volume = {34},

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pages = {757--821},

year = {2009},

doi = {10.1613/jair.2736}

}

Downloadtitle = {Conservative inference rule for uncertain reasoning under incompleteness},

journal = {Journal of Artificial Intelligence Research},

volume = {34},

author = {Zaffalon, M. and Miranda, E.},

pages = {757--821},

year = {2009},

doi = {10.1613/jair.2736}

}

top## 2008

## Decision-theoretic specification of credal networks: a unified language for uncertain modeling with sets of Bayesian networks

## Generalized loopy 2U: a new algorithm for approximate inference in credal networks

## Improving Bayesian network parameter learning using constraints

## Strategy selection in influence diagrams using imprecise probabilities

## Constrained maximum likelihood learning of Bayesian networks for facial action recognition

## Learning reliable classifiers from small or incomplete data sets: the naive credal classifier 2

## JNCC2: an extension of naive Bayes classifier suited for small and incomplete data sets

## JNCC2: the Java implementation of naive credal classifier 2

## Naive credal classifier 2: an extension of naive Bayes for delivering robust classifications

## Credal model averaging: an extension of Bayesian model averaging to imprecise probabilities

## Probabilistic logic with independence

## Dealing with soft evidence in credal networks

## Spatially distributed identification of debris flow source areas by credal networks

## Array-CGH identifies regions, including the FOXP1 locus, associated with different clinical outcome in diffuse large B-cell lymphomas (DLBCL) treated with R-CHOP

(2008). Decision-theoretic specification of credal networks: a unified language for uncertain modeling with sets of Bayesian networks. *International Journal of Approximate Reasoning* **49**(2), pp. 345–361.

@ARTICLE{antonucci2008b,

title = {Decision-theoretic specification of credal networks: a unified language for uncertain modeling with sets of {B}ayesian networks},

journal = {International Journal of Approximate Reasoning},

volume = {49},

author = {Antonucci, A. and Zaffalon, M.},

number = {2},

pages = {345--361},

year = {2008},

doi = {10.1016/j.ijar.2008.02.005}

}

Downloadtitle = {Decision-theoretic specification of credal networks: a unified language for uncertain modeling with sets of {B}ayesian networks},

journal = {International Journal of Approximate Reasoning},

volume = {49},

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number = {2},

pages = {345--361},

year = {2008},

doi = {10.1016/j.ijar.2008.02.005}

}

(2008). Generalized loopy 2U: a new algorithm for approximate inference in credal networks. In Jaeger, M., Nielsen, T.D. (Eds), *PGM'08: Proceedings of the Fourth European Workshop on Probabilistic Graphical Models*, Hirtshals (Denmark), pp. 17–24.

@INPROCEEDINGS{antonucci2008a,

title = {Generalized loopy {2U}: a new algorithm for approximate inference in credal networks},

editor = {Jaeger, M. and Nielsen, T.D.},

address = {Hirtshals (Denmark)},

booktitle = {{PGM'08}: Proceedings of the Fourth European Workshop on Probabilistic Graphical Models},

author = {Antonucci, A. and Zaffalon, M. and Yi, S. and de Campos, C.P.},

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url = {http://pgm08.cs.aau.dk/Papers/38_Paper.pdf}

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Downloadtitle = {Generalized loopy {2U}: a new algorithm for approximate inference in credal networks},

editor = {Jaeger, M. and Nielsen, T.D.},

address = {Hirtshals (Denmark)},

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author = {Antonucci, A. and Zaffalon, M. and Yi, S. and de Campos, C.P.},

pages = {17--24},

year = {2008},

url = {http://pgm08.cs.aau.dk/Papers/38_Paper.pdf}

}

(2008). Improving Bayesian network parameter learning using constraints. In *19th International Conference on Pattern Recognition (ICPR)*, pp. 1–4.

@INPROCEEDINGS{decampos2008d,

title = {Improving {B}ayesian network parameter learning using constraints},

booktitle = {19th International Conference on Pattern Recognition ({ICPR})},

author = {de Campos, C.P. and Ji, Q.},

pages = {1--4},

year = {2008},

doi = {10.1109/ICPR.2008.4761287}

}

Downloadtitle = {Improving {B}ayesian network parameter learning using constraints},

booktitle = {19th International Conference on Pattern Recognition ({ICPR})},

author = {de Campos, C.P. and Ji, Q.},

pages = {1--4},

year = {2008},

doi = {10.1109/ICPR.2008.4761287}

}

(2008). Strategy selection in influence diagrams using imprecise probabilities. In *Proceedings of the Twenty-fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-08)*, AUAI Press, Corvallis, Oregon, pp. 121–128.

@INPROCEEDINGS{decampos2008b,

title = {Strategy selection in influence diagrams using imprecise probabilities},

publisher = {AUAI Press},

address = {Corvallis, Oregon},

booktitle = {Proceedings of the Twenty-{f}ourth Conference Annual Conference on Uncertainty in Artificial Intelligence ({UAI}-08)},

author = {de Campos, C. and Ji, Q.},

pages = {121--128},

year = {2008},

url = {http://uai.sis.pitt.edu/papers/08/p121-de_campos.pdf}

}

Downloadtitle = {Strategy selection in influence diagrams using imprecise probabilities},

publisher = {AUAI Press},

address = {Corvallis, Oregon},

booktitle = {Proceedings of the Twenty-{f}ourth Conference Annual Conference on Uncertainty in Artificial Intelligence ({UAI}-08)},

author = {de Campos, C. and Ji, Q.},

pages = {121--128},

year = {2008},

url = {http://uai.sis.pitt.edu/papers/08/p121-de_campos.pdf}

}

(2008). Constrained maximum likelihood learning of Bayesian networks for facial action recognition. In Forsyth, D., Torr, P., Zisserman, A. (Eds), *Computer Vision - ECCV 2008*, Lecture Notes in Computer Science **5304**, Springer, Berlin / Heidelberg, pp. 168–181.

@INPROCEEDINGS{decampos2008c,

title = {Constrained maximum likelihood learning of {B}ayesian networks for facial action recognition},

editor = {Forsyth, D. and Torr, P. and Zisserman, A.},

publisher = {Springer, Berlin / Heidelberg},

series = {Lecture Notes in Computer Science},

volume = {5304},

booktitle = {Computer Vision - {ECCV} 2008},

author = {de Campos, C. and Tong, Y. and Ji, Q.},

pages = {168--181},

year = {2008},

doi = {10.1007/978-3-540-88690-7_13}

}

Downloadtitle = {Constrained maximum likelihood learning of {B}ayesian networks for facial action recognition},

editor = {Forsyth, D. and Torr, P. and Zisserman, A.},

publisher = {Springer, Berlin / Heidelberg},

series = {Lecture Notes in Computer Science},

volume = {5304},

booktitle = {Computer Vision - {ECCV} 2008},

author = {de Campos, C. and Tong, Y. and Ji, Q.},

pages = {168--181},

year = {2008},

doi = {10.1007/978-3-540-88690-7_13}

}

(2008). Learning reliable classifiers from small or incomplete data sets: the naive credal classifier 2. *Journal of Machine Learning Research* **9**, pp. 581–621.

@ARTICLE{corani2008d,

title = {Learning reliable classifiers from small or incomplete data sets: the naive credal classifier 2},

journal = {Journal of Machine Learning Research},

volume = {9},

author = {Corani, G. and Zaffalon, M.},

pages = {581--621},

year = {2008},

url = {http://jmlr.csail.mit.edu/papers/volume9/corani08b/corani08b.pdf}

}

Downloadtitle = {Learning reliable classifiers from small or incomplete data sets: the naive credal classifier 2},

journal = {Journal of Machine Learning Research},

volume = {9},

author = {Corani, G. and Zaffalon, M.},

pages = {581--621},

year = {2008},

url = {http://jmlr.csail.mit.edu/papers/volume9/corani08b/corani08b.pdf}

}

(2008). JNCC2: an extension of naive Bayes classifier suited for small and incomplete data sets. *Environmental Modelling & Software* **23**(7), pp. 960–961.

@ARTICLE{corani2008b,

title = {{JNCC2}: an extension of naive {B}ayes classifier suited for small and incomplete data sets},

journal = {Environmental Modelling & Software},

volume = {23},

author = {Corani, G. and Zaffalon, M.},

number = {7},

pages = {960--961},

year = {2008},

doi = {10.1016/j.envsoft.2008.01.004}

}

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journal = {Environmental Modelling & Software},

volume = {23},

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number = {7},

pages = {960--961},

year = {2008},

doi = {10.1016/j.envsoft.2008.01.004}

}

(2008). JNCC2: the Java implementation of naive credal classifier 2. *Journal of Machine Learning Research* **9**, pp. 2695–2698.

@ARTICLE{corani2008c,

title = {{JNCC2}: the {J}ava implementation of naive credal classifier 2},

journal = {Journal of Machine Learning Research},

volume = {9},

author = {Corani, G. and Zaffalon, M.},

pages = {2695--2698},

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url = {http://jmlr.csail.mit.edu/papers/volume9/corani08b/corani08b.pdf}

}

Downloadtitle = {{JNCC2}: the {J}ava implementation of naive credal classifier 2},

journal = {Journal of Machine Learning Research},

volume = {9},

author = {Corani, G. and Zaffalon, M.},

pages = {2695--2698},

year = {2008},

url = {http://jmlr.csail.mit.edu/papers/volume9/corani08b/corani08b.pdf}

}

(2008). Naive credal classifier 2: an extension of naive Bayes for delivering robust classifications. In *Proc. International Conference on Data Mining 2008 (DMIN '08)*.

@INPROCEEDINGS{corani2008a,

title = {Naive credal classifier 2: an extension of naive {B}ayes for delivering robust classifications},

booktitle = {Proc. International Conference on Data Mining 2008 ({DMIN} '08)},

author = {Corani, G. and Zaffalon, M.},

year = {2008}

}

Downloadtitle = {Naive credal classifier 2: an extension of naive {B}ayes for delivering robust classifications},

booktitle = {Proc. International Conference on Data Mining 2008 ({DMIN} '08)},

author = {Corani, G. and Zaffalon, M.},

year = {2008}

}

(2008). Credal model averaging: an extension of Bayesian model averaging to imprecise probabilities. In Daelemans, W., Goethals, B., Morik, K. (Eds), *Machine Learning and Knowledge Discovery in Databases*, Lecture Notes in Computer Science **5211**, Springer, Berlin / Heidelberg, pp. 257–271.

@INCOLLECTION{corani2008e,

title = {Credal model averaging: an extension of {B}ayesian model averaging to imprecise probabilities},

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series = {Lecture Notes in Computer Science},

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doi = {10.1007/978-3-540-87479-9_35}

}

(2008). Probabilistic logic with independence. *International Journal of Approximate Reasoning* **49**(1), pp. 3–17.

@ARTICLE{decampos2008a,

title = {Probabilistic logic with independence},

journal = {International Journal of Approximate Reasoning},

volume = {49},

author = {Cozman, F.G. and de Campos, C.P. and da Rocha, J.C.F.},

number = {1},

pages = {3--17},

year = {2008},

doi = {10.1016/j.ijar.2007.08.002}

}

Downloadtitle = {Probabilistic logic with independence},

journal = {International Journal of Approximate Reasoning},

volume = {49},

author = {Cozman, F.G. and de Campos, C.P. and da Rocha, J.C.F.},

number = {1},

pages = {3--17},

year = {2008},

doi = {10.1016/j.ijar.2007.08.002}

}

(2008). Dealing with soft evidence in credal networks. In *Conferencia Latinoamericana De Informatica*.

@INPROCEEDINGS{decampos2008e,

title = {Dealing with soft evidence in credal networks},

booktitle = {Conferencia Latinoamericana De Informatica},

author = {da Rocha, J.C.F. and Guimaraes, A.M. and de Campos, C.P.},

year = {2008}

}

Downloadtitle = {Dealing with soft evidence in credal networks},

booktitle = {Conferencia Latinoamericana De Informatica},

author = {da Rocha, J.C.F. and Guimaraes, A.M. and de Campos, C.P.},

year = {2008}

}

(2008). Spatially distributed identification of debris flow source areas by credal networks. In Sanchez-Marrè, M., Béjar, J., Comas, J., Rizzoli, A., Guariso, G. (Eds), *iEMSs 2008: International Congress on Environmental Modelling and Software Integrating Sciences and Information Technology for Environmental Assessment and Decision Making (transactions of the 4th Biennial Meeting of the International Environmental Modelling and Software Society)*, iEMSs, Manno, Switzerland, pp. 380–387.

@INPROCEEDINGS{antonucci2008c,

title = {Spatially distributed identification of debris flow source areas by credal networks},

editor = {Sanchez-Marr\`e, M. and B\'ejar, J. and Comas, J. and Rizzoli, A. and Guariso, G.},

publisher = {iEMSs},

address = {Manno, Switzerland},

booktitle = {{iEMSs} 2008: International Congress on Environmental Modelling and Software Integrating Sciences and Information Technology for Environmental Assessment and Decision Making ({t}ransactions of the 4th Biennial Meeting of the International Environmental Modelling and Software Society)},

author = {Salvetti, A. and Antonucci, A. and Zaffalon, M.},

pages = {380--387},

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url = {http://www.iemss.org/iemss2008/uploads/Main/S04-16-Salvetti_et_al-IEMSS2008.pdf}

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Downloadtitle = {Spatially distributed identification of debris flow source areas by credal networks},

editor = {Sanchez-Marr\`e, M. and B\'ejar, J. and Comas, J. and Rizzoli, A. and Guariso, G.},

publisher = {iEMSs},

address = {Manno, Switzerland},

booktitle = {{iEMSs} 2008: International Congress on Environmental Modelling and Software Integrating Sciences and Information Technology for Environmental Assessment and Decision Making ({t}ransactions of the 4th Biennial Meeting of the International Environmental Modelling and Software Society)},

author = {Salvetti, A. and Antonucci, A. and Zaffalon, M.},

pages = {380--387},

year = {2008},

url = {http://www.iemss.org/iemss2008/uploads/Main/S04-16-Salvetti_et_al-IEMSS2008.pdf}

}

(2008). Array-CGH identifies regions, including the FOXP1 locus, associated with different clinical outcome in diffuse large B-cell lymphomas (DLBCL) treated with R-CHOP. *Blood (ASH Annual Meeting Abstracts)* **112**, pp. 478.

@ARTICLE{decampos2008f,

title = {Array-{CGH} identifies regions, including the {FOXP1} locus, associated with different clinical outcome in diffuse large {B}-cell lymphomas ({DLBCL}) treated with {R}-{CHOP}},

journal = {Blood ({ASH} Annual Meeting Abstracts)},

volume = {112},

author = {Scandurra, M. and Rancoita, P.M.V. and Greiner, T.C. and Chan, W.C. and Vose, J.M. and Inghirami, G. and Chiappella, A. and Baldini, L. and Ponzoni, M. and Ferreri, A.J.M. and Montes-Moreno, S. and Piris, M.A. and Franceschetti, S. and Gaidano, G. and Facchetti, F. and Tucci, A. and Lazure, T. and Lambotte, O. and Uccella, S. and Pinotti, G. and Pruneri, G. and Martinelli, G. and Nomdedeu, J. and Tibiletti, M.G. and Chigrinova, E. and Campos, C.P.D. and Rinaldi, A. and Zucca, E. and Kwee, I. and Bertoni, F.},

pages = {478},

year = {2008}

}

Downloadtitle = {Array-{CGH} identifies regions, including the {FOXP1} locus, associated with different clinical outcome in diffuse large {B}-cell lymphomas ({DLBCL}) treated with {R}-{CHOP}},

journal = {Blood ({ASH} Annual Meeting Abstracts)},

volume = {112},

author = {Scandurra, M. and Rancoita, P.M.V. and Greiner, T.C. and Chan, W.C. and Vose, J.M. and Inghirami, G. and Chiappella, A. and Baldini, L. and Ponzoni, M. and Ferreri, A.J.M. and Montes-Moreno, S. and Piris, M.A. and Franceschetti, S. and Gaidano, G. and Facchetti, F. and Tucci, A. and Lazure, T. and Lambotte, O. and Uccella, S. and Pinotti, G. and Pruneri, G. and Martinelli, G. and Nomdedeu, J. and Tibiletti, M.G. and Chigrinova, E. and Campos, C.P.D. and Rinaldi, A. and Zucca, E. and Kwee, I. and Bertoni, F.},

pages = {478},

year = {2008}

}

top## 2007

## Credal networks for military identification problems

## Credal networks for operational risk measurement and management

## Credal networks for hazard assessment of debris flows

## Fast algorithms for robust classification with Bayesian nets

## Inference in credal networks through integer programming

## Computing lower and upper expectations under epistemic independence

## Multilinear and integer programming for markov decision processes with imprecise probabilities

## Independence in relational languages with finite domains

## Coherence graphs

## Learning about a categorical latent variable under prior near-ignorance

## Notes on “Notes on conditional previsions”

(2007). Credal networks for military identification problems. In de Cooman, G., Vejnarová, J., Zaffalon, M. (Eds), *ISIPTA '07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications*, Action M Agency, Prague (Czech Republic), pp. 1–10.

@INPROCEEDINGS{antonucci2007b,

title = {Credal networks for military identification problems},

editor = {de Cooman, G. and Vejnarov\'a, J. and Zaffalon, M.},

publisher = {Action M Agency},

address = {Prague (Czech Republic)},

booktitle = {{ISIPTA} '07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications},

author = {Antonucci, A. and Br\"uhlmann, R. and Piatti, A. and Zaffalon, M.},

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url = {http://www.sipta.org/isipta07/proceedings/papers/s066.pdf}

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editor = {de Cooman, G. and Vejnarov\'a, J. and Zaffalon, M.},

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booktitle = {{ISIPTA} '07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications},

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pages = {1--10},

year = {2007},

url = {http://www.sipta.org/isipta07/proceedings/papers/s066.pdf}

}

(2007). Credal networks for operational risk measurement and management. In Apolloni, B., Howlett, R.J., Jain, L.C. (Eds), *Proceedings of the 11th International Conference on Knowledge-based and Intelligent Information & Engineering Systems: KES2007, Lectures Notes in Computer Science* **4693**, Springer, pp. 604–611.

@INPROCEEDINGS{antonucci2007c,

title = {Credal networks for operational risk measurement and management},

editor = {Apolloni, B. and Howlett, R.J. and Jain, L.C.},

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volume = {4693},

booktitle = {Proceedings of the 11th International Conference on Knowledge-{b}ased and Intelligent Information & Engineering Systems: {KES2007}, Lectures Notes in Computer Science},

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year = {2007},

doi = {10.1007/978-3-540-74827-4_76}

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(2007). Credal networks for hazard assessment of debris flows. In *Progress of Artificial Intelligence in Sustainability Science*, Nova Science, Kropp, J., Scheffran, J., New York, pp. 125–132.

@INBOOK{antonucci2007d,

title = {Credal networks for hazard assessment of debris flows},

publisher = {Kropp, J., Scheffran, J.},

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Downloadtitle = {Credal networks for hazard assessment of debris flows},

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series = {Nova Science},

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author = {Antonucci, A. and Salvetti, A. and Zaffalon, M.},

pages = {125--132},

year = {2007}

}

(2007). Fast algorithms for robust classification with Bayesian nets. *International Journal of Approximate Reasoning* **44**(3), pp. 200–223.

@ARTICLE{antonucci2007a,

title = {Fast algorithms for robust classification with {B}ayesian nets},

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(2007). Inference in credal networks through integer programming. In *ISIPTA '07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications*, Prague, pp. 145–154.

@INPROCEEDINGS{decampos2007b,

title = {Inference in credal networks through integer programming},

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author = {de Campos, C.P. and Cozman, F.G.},

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Downloadtitle = {Inference in credal networks through integer programming},

address = {Prague},

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pages = {145--154},

year = {2007},

url = {http://www.sipta.org/isipta07/proceedings/papers/s065.pdf}

}

(2007). Computing lower and upper expectations under epistemic independence. *International Journal of Approximate Reasoning* **44**(3), pp. 244–260.

@ARTICLE{decampos2007a,

title = {Computing lower and upper expectations under epistemic independence},

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Downloadtitle = {Computing lower and upper expectations under epistemic independence},

journal = {International Journal of Approximate Reasoning},

volume = {44},

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pages = {244--260},

year = {2007},

doi = {10.1016/j.ijar.2006.07.013}

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(2007). Multilinear and integer programming for markov decision processes with imprecise probabilities. In *ISIPTA '07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications*, pp. 395–404.

@INPROCEEDINGS{decampos2007c,

title = {Multilinear and integer programming for markov decision processes with imprecise probabilities},

booktitle = {{ISIPTA} '07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications},

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booktitle = {{ISIPTA} '07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications},

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pages = {395--404},

year = {2007},

url = {http://www.sipta.org/isipta07/proceedings/papers/s062.pdf}

}

(2007). Independence in relational languages with finite domains. In *Encontro Nacional De Inteligência Artificial*, pp. 1420–1429.

@INPROCEEDINGS{decampos2007d,

title = {Independence in relational languages with finite domains},

booktitle = {Encontro Nacional De Intelig\^encia Artificial},

author = {Luna, J.E.O. and de Campos, C.P. and Cozman, F.G.},

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Downloadtitle = {Independence in relational languages with finite domains},

booktitle = {Encontro Nacional De Intelig\^encia Artificial},

author = {Luna, J.E.O. and de Campos, C.P. and Cozman, F.G.},

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(2007). Coherence graphs. In de Cooman, G., Vejnarova, J., Zaffalon, M. (Eds), *ISIPTA '07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications*, Action M Agency, Prague, pp. 297–306.

@INPROCEEDINGS{zaffalon2007c,

title = {Coherence graphs},

editor = {de Cooman, G. and Vejnarova, J. and Zaffalon, M.},

publisher = {Action M Agency},

address = {Prague},

booktitle = {{ISIPTA} '07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications},

author = {Miranda, E. and Zaffalon, M.},

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Downloadtitle = {Coherence graphs},

editor = {de Cooman, G. and Vejnarova, J. and Zaffalon, M.},

publisher = {Action M Agency},

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pages = {297--306},

year = {2007},

url = {http://www.sipta.org/isipta07/proceedings/papers/s060.pdf}

}

(2007). Learning about a categorical latent variable under prior near-ignorance. In de Cooman, G., Vejnarova, J., Zaffalon, M. (Eds), *ISIPTA '07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications*, Action M Agency, Prague, pp. 357–364.

@INPROCEEDINGS{zaffalon2007b,

title = {Learning about a categorical latent variable under prior near-ignorance},

editor = {de Cooman, G. and Vejnarova, J. and Zaffalon, M.},

publisher = {Action M Agency},

address = {Prague},

booktitle = {{ISIPTA} '07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications},

author = {Piatti, A. and Trojani, F. and Hutter, M. and Zaffalon, M.},

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year = {2007},

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Downloadtitle = {Learning about a categorical latent variable under prior near-ignorance},

editor = {de Cooman, G. and Vejnarova, J. and Zaffalon, M.},

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booktitle = {{ISIPTA} '07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications},

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pages = {357--364},

year = {2007},

url = {http://www.sipta.org/isipta07/proceedings/papers/s049.pdf}

}

(2007). Notes on “Notes on conditional previsions”. *International Journal of Approximate Reasoning* **44**(3), pp. 358–365.

@ARTICLE{zaffalon2007a,

title = {Notes on “{N}otes on conditional previsions”},

journal = {International Journal of Approximate Reasoning},

volume = {44},

author = {Vicig, P. and Cozman, F. and Zaffalon, M.},

number = {3},

pages = {358--365},

year = {2007},

doi = {10.1016/j.ijar.2006.07.018}

}

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journal = {International Journal of Approximate Reasoning},

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author = {Vicig, P. and Cozman, F. and Zaffalon, M.},

number = {3},

pages = {358--365},

year = {2007},

doi = {10.1016/j.ijar.2006.07.018}

}

top## 2006

## Binarization algorithms for approximate updating in credal nets

## Equivalence between Bayesian and credal nets on an updating problem

## Locally specified credal networks

## Classification of dementia types from cognitive profiles data

## Probabilistic logic with strong independence

(2006). Binarization algorithms for approximate updating in credal nets. In Penserini, L., Peppas, P., Perini, A. (Eds), *STAIRS'06: Proceedings of the Third European Starting AI Researcher Symposium*, IOS Press, Amsterdam (Netherlands), pp. 120–131.

@INPROCEEDINGS{antonucci2006b,

title = {Binarization algorithms for approximate updating in credal nets},

editor = {Penserini, L. and Peppas, P. and Perini, A.},

publisher = {IOS Press},

address = {Amsterdam (Netherlands)},

booktitle = {{STAIRS'06}: Proceedings of the Third European Starting {AI} Researcher Symposium},

author = {Antonucci, A. and Zaffalon, M. and Ide, J.S. and Cozman, F.G.},

pages = {120--131},

year = {2006}

}

Downloadtitle = {Binarization algorithms for approximate updating in credal nets},

editor = {Penserini, L. and Peppas, P. and Perini, A.},

publisher = {IOS Press},

address = {Amsterdam (Netherlands)},

booktitle = {{STAIRS'06}: Proceedings of the Third European Starting {AI} Researcher Symposium},

author = {Antonucci, A. and Zaffalon, M. and Ide, J.S. and Cozman, F.G.},

pages = {120--131},

year = {2006}

}

(2006). Equivalence between Bayesian and credal nets on an updating problem. In Lawry, J., Miranda, E., Bugarin, A., Li, S., Gil, M.A., Grzegorzewski, P., Hryniewicz, O. (Eds), *Proceedings of Third International Conference on Soft Methods in Probability and Statistics (SMPS-2006)*, Springer, pp. 223–230.

@INPROCEEDINGS{antonucci2006a,

title = {Equivalence between {B}ayesian and credal nets on an updating problem},

editor = {Lawry, J. and Miranda, E. and Bugarin, A. and Li, S. and Gil, M.A. and Grzegorzewski, P. and Hryniewicz, O.},

publisher = {Springer},

booktitle = {Proceedings of Third International Conference on Soft Methods in Probability and Statistics ({SMPS}-2006)},

author = {Antonucci, A. and Zaffalon, M.},

pages = {223--230},

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Downloadtitle = {Equivalence between {B}ayesian and credal nets on an updating problem},

editor = {Lawry, J. and Miranda, E. and Bugarin, A. and Li, S. and Gil, M.A. and Grzegorzewski, P. and Hryniewicz, O.},

publisher = {Springer},

booktitle = {Proceedings of Third International Conference on Soft Methods in Probability and Statistics ({SMPS}-2006)},

author = {Antonucci, A. and Zaffalon, M.},

pages = {223--230},

year = {2006},

doi = {10.1007/3-540-34777-1_27}

}

(2006). Locally specified credal networks. In Studený, M., Vomlel, J. (Eds), *PGM'06: Proceedings of the Third European Workshop on Probabilistic Graphical Models*, Action M Agency, Prague (Czech Republic), pp. 25–34.

@INPROCEEDINGS{antonucci2006c,

title = {Locally specified credal networks},

editor = {Studen\'y, M. and Vomlel, J.},

publisher = {Action M Agency},

address = {Prague (Czech Republic)},

booktitle = {{PGM'06}: Proceedings of the Third European Workshop on Probabilistic Graphical Models},

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Downloadtitle = {Locally specified credal networks},

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pages = {25--34},

year = {2006},

url = {http://www.utia.cas.cz/files/mtr/pgm06/26_paper.pdf}

}

(2006). Classification of dementia types from cognitive profiles data. In Fuernkranz, J., Scheffer, T., Spiliopoulou, M. (Eds), *Knowledge Discovery in Databases: PKDD 2006*, Lecture Notes in Computer Science **4213**, Springer, Berlin / Heidelberg, pp. 470–477.

@INCOLLECTION{corani2006a,

title = {Classification of dementia types from cognitive profiles data},

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pages = {470--477},

year = {2006},

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(2006). Probabilistic logic with strong independence. In Sichman, J., Coelho, H., Rezende, S. (Eds), *Advances in Artificial Intelligence - IBERAMIA-SBIA 2006*, Lecture Notes in Computer Science **4140**, Springer, Berlin / Heidelberg, pp. 612–621.

@INPROCEEDINGS{decampos2006b,

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Downloadtitle = {Probabilistic logic with strong independence},

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year = {2006},

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top## 2005

## Fast algorithms for robust classification with Bayesian nets

## Belief updating and learning in semi-qualitative probabilistic networks

## Computing lower and upper expectations under epistemic independence

## The inferential complexity of Bayesian and credal networks

## Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning

## Distribution of mutual information from complete and incomplete data

## Partially ordered preferences in decision trees: computing strategies with imprecision in probabilities

## Limits of learning from imperfect observations under prior ignorance: the case of the imprecise Dirichlet model

## IDS: a divide-and-conquer algorithm for inference in polytree-shaped credal networks

## Credible classification for environmental problems

## Conservative rules for predictive inference with incomplete data

## Robust inference of trees

(2005). Fast algorithms for robust classification with Bayesian nets. In Cozman, F.G., Nau, R., Seidenfeld, T. (Eds), *ISIPTA '05: Proceedings of the Fifth International Symposium on Imprecise Probabilities and Their Applications*, SIPTA, pp. 11–20.

@INPROCEEDINGS{antonucci2005a,

title = {Fast algorithms for robust classification with {B}ayesian nets},

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Downloadtitle = {Fast algorithms for robust classification with {B}ayesian nets},

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pages = {11--20},

year = {2005},

url = {http://www.sipta.org/isipta05/proceedings/papers/s045.pdf}

}

(2005). Belief updating and learning in semi-qualitative probabilistic networks. In *Conference on Uncertainty in Artificial Intelligence*, pp. 153–160.

@INPROCEEDINGS{decampos2005e,

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(2005). Computing lower and upper expectations under epistemic independence. In *ISIPTA '05: Proceedings of the Fourth International Symposium on Imprecise Probabilities and Their Applications*, pp. 78–87.

@INPROCEEDINGS{decampos2005d,

title = {Computing lower and upper expectations under epistemic independence},

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Downloadtitle = {Computing lower and upper expectations under epistemic independence},

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year = {2005},

url = {http://www.sipta.org/isipta05/proceedings/papers/s006.pdf}

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(2005). The inferential complexity of Bayesian and credal networks. In *International Joint Conference on Artificial Intelligence*, pp. 1313–1318.

@INPROCEEDINGS{decampos2005b,

title = {The inferential complexity of {B}ayesian and credal networks},

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(2005). Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning. *Ecological Modelling* **185**(2-4), pp. 513–529.

@ARTICLE{corani2005b,

title = {Air quality prediction in {M}ilan: feed-forward neural networks, pruned neural networks and lazy learning},

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year = {2005},

doi = {10.1016/j.ecolmodel.2005.01.008}

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(2005). Distribution of mutual information from complete and incomplete data. *Computational Statistics and Data Analysis* **48**(3), pp. 633–657.

@ARTICLE{zaffalon2005b,

title = {Distribution of mutual information from complete and incomplete data},

journal = {Computational Statistics and Data Analysis},

volume = {48},

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pages = {633--657},

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Downloadtitle = {Distribution of mutual information from complete and incomplete data},

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year = {2005},

doi = {10.1016/j.csda.2004.03.010}

}

(2005). Partially ordered preferences in decision trees: computing strategies with imprecision in probabilities. In *IJCAI Workshop About Advances on Preference Handling*, pp. 1313–1318.

@INPROCEEDINGS{decampos2005f,

title = {Partially ordered preferences in decision trees: computing strategies with imprecision in probabilities},

booktitle = {{IJCAI} Workshop About Advances on Preference Handling},

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}

Downloadtitle = {Partially ordered preferences in decision trees: computing strategies with imprecision in probabilities},

booktitle = {{IJCAI} Workshop About Advances on Preference Handling},

author = {Kikuti, D. and Cozman, F.G. and de Campos, C.P.},

pages = {1313--1318},

year = {2005},

url = {http://www.l3s.de/web/upload/documents/Pref05.pdf}

}

(2005). Limits of learning from imperfect observations under prior ignorance: the case of the imprecise Dirichlet model. In Cozman, F.G., Nau, R., Seidenfeld, T. (Eds), *ISIPTA '05: Proceedings of the Fourth International Symposium on Imprecise Probabilities and Their Applications*, SIPTA, pp. 276–286.

@INPROCEEDINGS{zaffalon2005d,

title = {Limits of learning from imperfect observations under prior ignorance: the case of the imprecise {D}irichlet model},

editor = {Cozman, F.G. and Nau, R. and Seidenfeld, T.},

publisher = {SIPTA},

booktitle = {{ISIPTA} '05: Proceedings of the Fourth International Symposium on Imprecise Probabilities and Their Applications},

author = {Piatti, A. and Zaffalon, M. and Trojani, F.},

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booktitle = {{ISIPTA} '05: Proceedings of the Fourth International Symposium on Imprecise Probabilities and Their Applications},

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pages = {276--286},

year = {2005},

url = {http://www.sipta.org/isipta05/proceedings/papers/s020.pdf}

}

(2005). IDS: a divide-and-conquer algorithm for inference in polytree-shaped credal networks. In *Anais Do Encontro Nacional De Inteligencia Artificial (brazilian AI National Meeting)*, pp. 553–562.

@INPROCEEDINGS{decampos2005a,

title = {{IDS}: a divide-and-conquer algorithm for inference in polytree-shaped credal networks},

booktitle = {Anais Do Encontro Nacional De Inteligencia Artificial ({b}razilian {AI} National Meeting)},

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Downloadtitle = {{IDS}: a divide-and-conquer algorithm for inference in polytree-shaped credal networks},

booktitle = {Anais Do Encontro Nacional De Inteligencia Artificial ({b}razilian {AI} National Meeting)},

author = {da Rocha, J.C.F. and de Campos, C.P. and Cozman, F.G.},

pages = {553--562},

year = {2005}

}

(2005). Credible classification for environmental problems. *Environmental modelling and software* **20**(8), pp. 1003–1012.

@ARTICLE{zaffalon2005a,

title = {Credible classification for environmental problems},

journal = {Environmental {m}odelling and {s}oftware},

volume = {20},

author = {Zaffalon, M.},

number = {8},

pages = {1003--1012},

year = {2005},

doi = {10.1016/j.envsoft.2004.10.006}

}

Downloadtitle = {Credible classification for environmental problems},

journal = {Environmental {m}odelling and {s}oftware},

volume = {20},

author = {Zaffalon, M.},

number = {8},

pages = {1003--1012},

year = {2005},

doi = {10.1016/j.envsoft.2004.10.006}

}

(2005). Conservative rules for predictive inference with incomplete data. In Cozman, F.G., Nau, R., Seidenfeld, T. (Eds), *ISIPTA '05: Proceedings of the Fourth International Symposium on Imprecise Probabilities and Their Applications*, SIPTA, pp. 406–415.

@INPROCEEDINGS{zaffalon2005c,

title = {Conservative rules for predictive inference with incomplete data},

editor = {Cozman, F.G. and Nau, R. and Seidenfeld, T.},

publisher = {SIPTA},

booktitle = {{ISIPTA} '05: Proceedings of the Fourth International Symposium on Imprecise Probabilities and Their Applications},

author = {Zaffalon, M.},

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year = {2005},

url = {http://www.sipta.org/isipta05/proceedings/papers/s038.pdf}

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Downloadtitle = {Conservative rules for predictive inference with incomplete data},

editor = {Cozman, F.G. and Nau, R. and Seidenfeld, T.},

publisher = {SIPTA},

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author = {Zaffalon, M.},

pages = {406--415},

year = {2005},

url = {http://www.sipta.org/isipta05/proceedings/papers/s038.pdf}

}

(2005). Robust inference of trees. *Annals of Mathematics and Artificial Intelligence* **45**(1–2), pp. 215–239.

@ARTICLE{zaffalon2005e,

title = {Robust inference of trees},

journal = {Annals of Mathematics and Artificial Intelligence},

volume = {45},

author = {Zaffalon, M. and Hutter, M.},

number = {1--2},

pages = {215--239},

year = {2005},

doi = {10.1007/s10472-005-9007-9}

}

Downloadtitle = {Robust inference of trees},

journal = {Annals of Mathematics and Artificial Intelligence},

volume = {45},

author = {Zaffalon, M. and Hutter, M.},

number = {1--2},

pages = {215--239},

year = {2005},

doi = {10.1007/s10472-005-9007-9}

}

top## 2004

## Assessing debris flow hazard by credal nets

## Hazard assessment of debris flows by credal networks

## Inference in credal networks using multilinear programming

## Updating beliefs with incomplete observations

## Local computation in credal networks

## Propositional and relational Bayesian networks associated with imprecise and qualitative probabilistic assessments

(2004). Assessing debris flow hazard by credal nets. In Lopez-Diaz, M., Gil, M.A., Grzegorzewski, P., Hryniewicz, O., Lawry, J. (Eds), *Proceedings of the Second International Conference on Soft Methods in Probability and Statistics (SMPS-2004) - Soft Methodology and Random Information Systems*, Springer, pp. 125–132.

@INPROCEEDINGS{antonucci2004b,

title = {Assessing debris flow hazard by credal nets},

editor = {Lopez-Diaz, M. and Gil, M.A. and Grzegorzewski, P. and Hryniewicz, O. and Lawry, J.},

publisher = {Springer},

booktitle = {Proceedings of the Second International Conference on Soft Methods in Probability and Statistics ({SMPS}-2004) - Soft Methodology and Random Information Systems},

author = {Antonucci, A. and Salvetti, A. and Zaffalon, M.},

pages = {125--132},

year = {2004}

}

Downloadtitle = {Assessing debris flow hazard by credal nets},

editor = {Lopez-Diaz, M. and Gil, M.A. and Grzegorzewski, P. and Hryniewicz, O. and Lawry, J.},

publisher = {Springer},

booktitle = {Proceedings of the Second International Conference on Soft Methods in Probability and Statistics ({SMPS}-2004) - Soft Methodology and Random Information Systems},

author = {Antonucci, A. and Salvetti, A. and Zaffalon, M.},

pages = {125--132},

year = {2004}

}

(2004). Hazard assessment of debris flows by credal networks. In Pahl-Wostl, C., Schmidt, S., Rizzoli, A.E., Jakeman, A.J. (Eds), *iEMSs 2004: Complexity and Integrated Resources Management, Transactions of the 2nd Biennial Meeting of the International Environmental Modelling and Software Society*, iEMSs, pp. 98–103.

@INPROCEEDINGS{antonucci2004a,

title = {Hazard assessment of debris flows by credal networks},

editor = {Pahl-Wostl, C. and Schmidt, S. and Rizzoli, A.E. and Jakeman, A.J.},

publisher = {iEMSs},

booktitle = {{iEMSs} 2004: Complexity and Integrated Resources Management, Transactions of the 2nd Biennial Meeting of the International Environmental Modelling and Software Society},

author = {Antonucci, A. and Salvetti, A. and Zaffalon, M.},

pages = {98--103},

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url = {http://www.iemss.org/iemss2004/pdf/ai/antohaza.pdf}

}

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editor = {Pahl-Wostl, C. and Schmidt, S. and Rizzoli, A.E. and Jakeman, A.J.},

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booktitle = {{iEMSs} 2004: Complexity and Integrated Resources Management, Transactions of the 2nd Biennial Meeting of the International Environmental Modelling and Software Society},

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pages = {98--103},

year = {2004},

url = {http://www.iemss.org/iemss2004/pdf/ai/antohaza.pdf}

}

(2004). Inference in credal networks using multilinear programming. In *Second Starting AI Researcher Symposium*, IOS Press, Valencia, pp. 50–61.

@INPROCEEDINGS{decampos2004a,

title = {Inference in credal networks using multilinear programming},

publisher = {IOS Press},

address = {Valencia},

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publisher = {IOS Press},

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author = {de Campos, C.P. and Cozman, F.G.},

pages = {50--61},

year = {2004}

}

(2004). Updating beliefs with incomplete observations. *Artificial Intelligence* **159**(1–2), pp. 75–125.

@ARTICLE{zaffalon2004a,

title = {Updating beliefs with incomplete observations},

journal = {Artificial Intelligence},

volume = {159},

author = {de Cooman, G. and Zaffalon, M.},

number = {1--2},

pages = {75--125},

year = {2004},

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}

Downloadtitle = {Updating beliefs with incomplete observations},

journal = {Artificial Intelligence},

volume = {159},

author = {de Cooman, G. and Zaffalon, M.},

number = {1--2},

pages = {75--125},

year = {2004},

doi = {10.1016/j.artint.2004.05.006}

}

(2004). Local computation in credal networks. In *Workshop on Local Computation for Logics and Uncertainty*, IOS Press, Valencia, pp. 5–11.

@INPROCEEDINGS{decampos2004b,

title = {Local computation in credal networks},

publisher = {IOS Press},

address = {Valencia},

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Downloadtitle = {Local computation in credal networks},

publisher = {IOS Press},

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}

(2004). Propositional and relational Bayesian networks associated with imprecise and qualitative probabilistic assessments. In *Conference on Uncertainty in Artificial Intelligence*, AUAI Press, Banff, pp. 104–111.

@INPROCEEDINGS{decampos2004c,

title = {Propositional and relational {B}ayesian networks associated with imprecise and qualitative probabilistic assessments},

publisher = {AUAI Press},

address = {Banff},

booktitle = {Conference on Uncertainty in Artificial Intelligence},

author = {Cozman, F.G. and de Campos, C.P. and Ide, J.S. and da Rocha, J.C.F.},

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year = {2004},

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Downloadtitle = {Propositional and relational {B}ayesian networks associated with imprecise and qualitative probabilistic assessments},

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address = {Banff},

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pages = {104--111},

year = {2004},

url = {http://uai.sis.pitt.edu/papers/04/p104-cozman.pdf}

}

top## 2003

## Updating with incomplete observations

## Bayesian treatment of incomplete discrete data applied to mutual information and feature selection

## Inference in polytrees with sets of probabilities

## Tree-based credal networks for classification

## Reliable diagnoses of dementia by the naive credal classifier inferred from incomplete cognitive data

(2003). Updating with incomplete observations. In Kjærulff, U., Meek, C. (Eds), *Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence (UAI-2002)*, Morgan Kaufmann, pp. 142–150.

@INPROCEEDINGS{zaffalon2003c,

title = {Updating with incomplete observations},

editor = {Kj\aerulff, U. and Meek, C.},

publisher = {Morgan Kaufmann},

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author = {de Cooman, G. and Zaffalon, M.},

pages = {142--150},

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url = {http://uai.sis.pitt.edu/papers/03/p142-de_cooman.pdf}

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Downloadtitle = {Updating with incomplete observations},

editor = {Kj\aerulff, U. and Meek, C.},

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pages = {142--150},

year = {2003},

url = {http://uai.sis.pitt.edu/papers/03/p142-de_cooman.pdf}

}

(2003). Bayesian treatment of incomplete discrete data applied to mutual information and feature selection. In Günter, A., Kruse, R., Neumann, B. (Eds), *Proceedings of the 26th German Conference on Artificial Intelligence (KI-2003)*, Lecture Notes in Computer Science **2821**, Springer-Verlag, Heidelberg, pp. 396–406.

@INPROCEEDINGS{zaffalon2003d,

title = {Bayesian treatment of incomplete discrete data applied to mutual information and feature selection},

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series = {Lecture Notes in Computer Science},

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Downloadtitle = {Bayesian treatment of incomplete discrete data applied to mutual information and feature selection},

editor = {G\"unter, A. and Kruse, R. and Neumann, B.},

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address = {Heidelberg},

series = {Lecture Notes in Computer Science},

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booktitle = {Proceedings of the 26th German Conference on Artificial Intelligence ({KI}-2003)},

author = {Hutter, M. and Zaffalon, M.},

pages = {396--406},

year = {2003},

doi = {10.1007/978-3-540-39451-8_29}

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(2003). Inference in polytrees with sets of probabilities. In *UAI*, pp. 217–224.

@INPROCEEDINGS{decampos2003a,

title = {Inference in polytrees with sets of probabilities},

booktitle = {{UAI}},

author = {da Rocha, J.C.F. and Cozman, F.G. and de Campos, C.P.},

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Downloadtitle = {Inference in polytrees with sets of probabilities},

booktitle = {{UAI}},

author = {da Rocha, J.C.F. and Cozman, F.G. and de Campos, C.P.},

pages = {217--224},

year = {2003},

url = {http://uai.sis.pitt.edu/papers/03/p217-ferreira_da_rocha.pdf}

}

(2003). Tree-based credal networks for classification. *Reliable Computing* **9**(6), pp. 487–509.

@ARTICLE{zaffalon2003b,

title = {Tree-based credal networks for classification},

journal = {Reliable Computing},

volume = {9},

author = {Zaffalon, M. and Fagiuoli, E.},

number = {6},

pages = {487--509},

year = {2003},

doi = {10.1023/A:1025822321743}

}

Downloadtitle = {Tree-based credal networks for classification},

journal = {Reliable Computing},

volume = {9},

author = {Zaffalon, M. and Fagiuoli, E.},

number = {6},

pages = {487--509},

year = {2003},

doi = {10.1023/A:1025822321743}

}

(2003). Reliable diagnoses of dementia by the naive credal classifier inferred from incomplete cognitive data. *Artificial Intelligence in Medicine* **29**(1–2), pp. 61–79.

@ARTICLE{zaffalon2003a,

title = {Reliable diagnoses of dementia by the naive credal classifier inferred from incomplete cognitive data},

journal = {Artificial Intelligence in Medicine},

volume = {29},

author = {Zaffalon, M. and Wesnes, K. and Petrini, O.},

number = {1--2},

pages = {61--79},

year = {2003},

doi = {10.1016/S0933-3657(03)00046-0}

}

Downloadtitle = {Reliable diagnoses of dementia by the naive credal classifier inferred from incomplete cognitive data},

journal = {Artificial Intelligence in Medicine},

volume = {29},

author = {Zaffalon, M. and Wesnes, K. and Petrini, O.},

number = {1--2},

pages = {61--79},

year = {2003},

doi = {10.1016/S0933-3657(03)00046-0}

}

top## 2002

## Computing with sets of probability measures

## Exact credal treatment of missing data

## The naive credal classifier

## Robust feature selection by mutual information distributions

## Credal classification for mining environmental data

(2002). Computing with sets of probability measures. In *SIAM Workshop on Validated Computing*, pp. 45–48.

@INPROCEEDINGS{decampos2002a,

title = {Computing with sets of probability measures},

booktitle = {{SIAM} Workshop on Validated Computing},

author = {Cozman, F.G. and da Rocha, J.C.F. and de Campos, C.P.},

pages = {45--48},

year = {2002},

url = {http://interval.louisiana.edu/conferences/VC02/abstracts/COZM.pdf}

}

Downloadtitle = {Computing with sets of probability measures},

booktitle = {{SIAM} Workshop on Validated Computing},

author = {Cozman, F.G. and da Rocha, J.C.F. and de Campos, C.P.},

pages = {45--48},

year = {2002},

url = {http://interval.louisiana.edu/conferences/VC02/abstracts/COZM.pdf}

}

(2002). Exact credal treatment of missing data. *Journal of Statistical Planning and Inference* **105**(1), pp. 105–122.

@ARTICLE{zaffalon2002b,

title = {Exact credal treatment of missing data},

journal = {Journal of Statistical Planning and Inference},

volume = {105},

author = {Zaffalon, M.},

number = {1},

pages = {105--122},

year = {2002},

doi = {10.1016/S0378-3758(01)00206-3}

}

Downloadtitle = {Exact credal treatment of missing data},

journal = {Journal of Statistical Planning and Inference},

volume = {105},

author = {Zaffalon, M.},

number = {1},

pages = {105--122},

year = {2002},

doi = {10.1016/S0378-3758(01)00206-3}

}

(2002). The naive credal classifier. *Journal of Statistical Planning and Inference* **105**(1), pp. 5–21.

@ARTICLE{zaffalon2002a,

title = {The naive credal classifier},

journal = {Journal of Statistical Planning and Inference},

volume = {105},

author = {Zaffalon, M.},

number = {1},

pages = {5--21},

year = {2002},

doi = {10.1016/S0378-3758(01)00201-4}

}

Downloadtitle = {The naive credal classifier},

journal = {Journal of Statistical Planning and Inference},

volume = {105},

author = {Zaffalon, M.},

number = {1},

pages = {5--21},

year = {2002},

doi = {10.1016/S0378-3758(01)00201-4}

}

(2002). Robust feature selection by mutual information distributions. In Darwiche, A., Friedman, N. (Eds), *Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence (UAI-2002)*, Morgan Kaufmann, pp. 577–584.

@INPROCEEDINGS{zaffalon2002c,

title = {Robust feature selection by mutual information distributions},

editor = {Darwiche, A. and Friedman, N.},

publisher = {Morgan Kaufmann},

booktitle = {Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence ({UAI}-2002)},

author = {Zaffalon, M. and Hutter, M.},

pages = {577--584},

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Downloadtitle = {Robust feature selection by mutual information distributions},

editor = {Darwiche, A. and Friedman, N.},

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booktitle = {Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence ({UAI}-2002)},

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pages = {577--584},

year = {2002},

url = {http://uai.sis.pitt.edu/papers/02/p577-zaffalon.pdf}

}

(2002). Credal classification for mining environmental data. In Rizzoli, A.E., Jakeman, A.J. (Eds), *iEMSs 2002: Integrated Assessment and Decision Support (transactions of the 1st Biennial Meeting of the International Environmental Modelling and Software Society)*, iEMSs, Manno, Switzerland, pp. 72–77.

@INPROCEEDINGS{zaffalon2002d,

title = {Credal classification for mining environmental data},

editor = {Rizzoli, A.E. and Jakeman, A.J.},

publisher = {iEMSs},

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booktitle = {{iEMSs} 2002: Integrated Assessment and Decision Support ({t}ransactions of the 1st Biennial Meeting of the International Environmental Modelling and Software Society)},

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Downloadtitle = {Credal classification for mining environmental data},

editor = {Rizzoli, A.E. and Jakeman, A.J.},

publisher = {iEMSs},

address = {Manno, Switzerland},

booktitle = {{iEMSs} 2002: Integrated Assessment and Decision Support ({t}ransactions of the 1st Biennial Meeting of the International Environmental Modelling and Software Society)},

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pages = {72--77},

year = {2002},

url = {http://www.iemss.org/iemss2002/proceedings/pdf/volume\%20due/95_zaffalon.pdf}

}

top## 2001

## Statistical inference of the naive credal classifier

## Robust discovery of tree-dependency structures

## Credal classification for dementia screening

(2001). Statistical inference of the naive credal classifier. In de Cooman, G., Fine, T.L., Seidenfeld, T. (Eds), *ISIPTA '01: Proceedings of the Second International Symposium on Imprecise Probabilities and Their Applications*, Shaker, The Netherlands, pp. 384–393.

@INPROCEEDINGS{zaffalon2001b,

title = {Statistical inference of the naive credal classifier},

editor = {de Cooman, G. and Fine, T.L. and Seidenfeld, T.},

publisher = {Shaker},

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booktitle = {{ISIPTA} '01: Proceedings of the Second International Symposium on Imprecise Probabilities and Their Applications},

author = {Zaffalon, M.},

pages = {384--393},

year = {2001},

url = {http://www.sipta.org/isipta01/proceedings/s035.pdf}

}

Downloadtitle = {Statistical inference of the naive credal classifier},

editor = {de Cooman, G. and Fine, T.L. and Seidenfeld, T.},

publisher = {Shaker},

address = {The Netherlands},

booktitle = {{ISIPTA} '01: Proceedings of the Second International Symposium on Imprecise Probabilities and Their Applications},

author = {Zaffalon, M.},

pages = {384--393},

year = {2001},

url = {http://www.sipta.org/isipta01/proceedings/s035.pdf}

}

(2001). Robust discovery of tree-dependency structures. In de Cooman, G., Fine, T.L., Seidenfeld, T. (Eds), *ISIPTA '01: Proceedings of the Second International Symposium on Imprecise Probabilities and Their Applications*, Shaker, The Netherlands, pp. 394–403.

@INPROCEEDINGS{zaffalon2001c,

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booktitle = {{ISIPTA} '01: Proceedings of the Second International Symposium on Imprecise Probabilities and Their Applications},

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pages = {394--403},

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Downloadtitle = {Robust discovery of tree-dependency structures},

editor = {de Cooman, G. and Fine, T.L. and Seidenfeld, T.},

publisher = {Shaker},

address = {The Netherlands},

booktitle = {{ISIPTA} '01: Proceedings of the Second International Symposium on Imprecise Probabilities and Their Applications},

author = {Zaffalon, M.},

pages = {394--403},

year = {2001},

url = {http://www.sipta.org/isipta01/proceedings/s037.pdf}

}

(2001). Credal classification for dementia screening. In Quaglini, S., Barahona, P., Andreassen, S. (Eds), *AIME'01*, Lecture Notes in Computer Science **2101**, Springer-Verlag, pp. 67–76.

@INPROCEEDINGS{zaffalon2001d,

title = {Credal classification for dementia screening},

editor = {Quaglini, S. and Barahona, P. and Andreassen, S.},

publisher = {Springer-Verlag},

series = {Lecture Notes in Computer Science},

volume = {2101},

booktitle = {{AIME'01}},

author = {Zaffalon, M. and Wesnes, K. and Petrini, O.},

pages = {67--76},

year = {2001},

doi = {10.1007/3-540-48229-6_10}

}

Downloadtitle = {Credal classification for dementia screening},

editor = {Quaglini, S. and Barahona, P. and Andreassen, S.},

publisher = {Springer-Verlag},

series = {Lecture Notes in Computer Science},

volume = {2101},

booktitle = {{AIME'01}},

author = {Zaffalon, M. and Wesnes, K. and Petrini, O.},

pages = {67--76},

year = {2001},

doi = {10.1007/3-540-48229-6_10}

}

top## 2000

## Tree-augmented naive credal classifiers

(2000). Tree-augmented naive credal classifiers. In Zadeh, L. A., Bouchon-Meunier, B. (Eds), *IPMU 2000: Proceedings of the 8th Information Processing and Management of Uncertainty in Knowledge-based Systems Conference*, Universidad Politècnica de Madrid, Madrid, pp. 1320–1327.

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booktitle = {{IPMU} 2000: Proceedings of the 8th Information Processing and Management of Uncertainty in Knowledge-{b}ased Systems Conference},

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author = {Fagiuoli, E. and Zaffalon, M.},

pages = {1320--1327},

year = {2000}

}

top## 1999

## A credal approach to naive classification

(1999). A credal approach to naive classification. In de Cooman, G., Cozman, F.G., Moral, S., Walley, P. (Eds), *ISIPTA '99: Proceedings of the First International Symposium on Imprecise Probabilities and Their Applications*, Universiteit Gent, Belgium, pp. 405–414.

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booktitle = {{ISIPTA} '99: Proceedings of the First International Symposium on Imprecise Probabilities and Their Applications},

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pages = {405--414},

year = {1999},

url = {ftp://decsai.ugr.es/pub/utai/other/smc/isipta99/019.pdf}

}

top## 1998

## A note about redundancy in influence diagrams

## 2U: an exact interval propagation algorithm for polytrees with binary variables

(1998). A note about redundancy in influence diagrams. *International Journal of Approximate Reasoning* **19**(3–4), pp. 231–246.

@ARTICLE{zaffalon1998b,

title = {A note about redundancy in influence diagrams},

journal = {International Journal of Approximate Reasoning},

volume = {19},

author = {Fagiuoli, E. and Zaffalon, M.},

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(1998). 2U: an exact interval propagation algorithm for polytrees with binary variables. *Artificial Intelligence* **106**(1), pp. 77–107.

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title = {{2U}: an exact interval propagation algorithm for polytrees with binary variables},

journal = {Artificial Intelligence},

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pages = {77--107},

year = {1998},

doi = {10.1016/S0004-3702(98)00089-7}

}

top## 2018

## Advancements in Bayesian network structure learning

(2018). Advancements in Bayesian network structure learning. Ph.D thesis, Università della Svizzera italiana.

@PHDTHESIS{scanagatta2018c,

title = {Advancements in {B}ayesian network structure learning},

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Downloadtitle = {Advancements in {B}ayesian network structure learning},

author = {Scanagatta, M.},

year = {2018},

institution = {Universit\`a della Svizzera italiana}

}

top## 2013

## Algorithms and Complexity Results for Discrete Probabilistic Reasoning Tasks

(2013). Algorithms and Complexity Results for Discrete Probabilistic Reasoning Tasks. Ph.D thesis, Università della Svizzera italiana.

@PHDTHESIS{maua2013,

title = {Algorithms and {C}omplexity {R}esults for {D}iscrete {P}robabilistic {R}easoning {T}asks},

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Downloadtitle = {Algorithms and {C}omplexity {R}esults for {D}iscrete {P}robabilistic {R}easoning {T}asks},

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url = {http://doc.rero.ch/record/203103?ln=en}

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