Approximating Credal Network Inferences by Linear Programming

Alessandro Antonucci, Cassio P. de Campos, David Huber, Marco Zaffalon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)
218 Downloads (Pure)


An algorithm for approximate credal network updating is presented. 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 complexity analysis for both the problem and the algorithm is reported together with numerical experiments, which confirm the good performance of the method. While the inner approximation produced by the algorithm gives rise to a classifier which might return a subset of the optimal class set, preliminary empirical results suggest that the accuracy of the optimal class set is seldom affected by the approximate probabilities
Original languageEnglish
Title of host publicationProceedings of the 12th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU)
EditorsLinda C. van der Gaag
Place of PublicationBerlin Heidelberg
Number of pages12
ISBN (Electronic)978-3-642-39091-3
ISBN (Print)978-3-642-39090-6
Publication statusPublished - 2013

Publication series

NameLecture Notes in Artificial Intelligence

Bibliographical note

Blind peer reviewed by multiple reviewers. Best paper award.


Dive into the research topics of 'Approximating Credal Network Inferences by Linear Programming'. Together they form a unique fingerprint.

Cite this