# Bayesian Inference by Symbolic Model Checking

arXiv: https://arxiv.org/abs/2007.15071 · HTML: https://mlsanity.com/paper/2007.15071/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [Fine-Tuning the Odds in Bayesian Networks](https://mlsanity.com/paper/2105.14371/) (2021)
- [Finding an ε-close Variation of Parameters in Bayesian Networks](https://mlsanity.com/paper/2305.10051/) (2023)
- [BayesL: a Logical Framework for the Verification of Bayesian Networks](https://mlsanity.com/paper/2506.23773/) (2025)

## Abstract (arXiv)

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.