An algorithm for approximated credal network inferences.
G-LP is an algorithm for approximate credal network updating. The problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. This simple idea can be iterated and quickly leads to very accurate inferences. The approach can also be specialized to classification with credal networks based on the maximality criterion.

A detailed description of the algorithm can be found in “Approximating credal network inferences by linear programming”.

Files G-LP download
Date1 May 2013
RequirementsCOIN-OR Clp
Java 1.6

Additional information

The package contains the following third party software:

Please note that G-LP is released for demonstrational purpouse only.


Alessandro Antonucci, PhD
Senior Lecturer-Researcher
David Huber, MSc
Senior Researcher