Related papers: Bayesian Inference by Symbolic Model Checking

Bayesian Inference by Symbolic Model Checking

URL: http://arxiv.org/abs/2007.15071v1
Date: Wed, 29 Jul 2020 19:38:17 GMT
Title: Bayesian Inference by Symbolic Model Checking
Authors: Bahare Salmani and Joost-Pieter Katoen
Abstract summary: We present a simple translation from Bayesian networks into tree-like Markov chains. We show that symbolic data structures such as multi-terminal BDDs (MTBDDs) are very effective to perform inference.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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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