Bayesian Inference by Symbolic Model Checking

2020/07/29 by Salmani, Bahare, Katoen, Joost-Pieter · 1 citation
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Formal Languages and Automata Theory (cs.FL)

paper · doi:10.48550/arxiv.2007.15071

Abstract

This paper applies probabilistic model checking techniques for discrete Markov chains to inference in Bayesian networks. We present a simple translation from Bayesian networks into tree-like Markov chains such that inference can be reduced to computing reachability probabilities. Using a prototypical implementation on top of the Storm model checker, we show that symbolic data structures such as multi-terminal BDDs (MTBDDs) are very effective to perform inference on large Bayesian network benchmarks. We compare our result to inference using probabilistic sentential decision diagrams and vtrees, a scalable symbolic technique in AI inference tools.

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• BayesL: a Logical Framework for the Verification of Bayesian Networks

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