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Bayesian Inference by Symbolic Model Checking

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Quantitative Evaluation of Systems (QEST 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12289))

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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.

This work is funded by the ERC AdG Projekt FRAPPANT (Grant Nr. 787914).

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Notes

  1. 1.

    https://github.com/hahaXD/psdd.

  2. 2.

    https://github.com/hahaXD/psdd_nips.

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Acknowledgement

The authors would like to thank Yujia Shen (UCLA) for his kind support with running the PSDD tools.

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Authors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Bahare Salmani & Joost-Pieter Katoen

Authors
  1. Bahare Salmani
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  2. Joost-Pieter Katoen
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Corresponding author

Correspondence to Bahare Salmani .

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Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Marco Gribaudo

  2. Chinese Academy of Sciences, Beijing, China

    David N. Jansen

  3. University of Münster, Münster, Germany

    Anne Remke

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Salmani, B., Katoen, JP. (2020). Bayesian Inference by Symbolic Model Checking. In: Gribaudo, M., Jansen, D.N., Remke, A. (eds) Quantitative Evaluation of Systems. QEST 2020. Lecture Notes in Computer Science(), vol 12289. Springer, Cham. https://doi.org/10.1007/978-3-030-59854-9_9

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  • DOI: https://doi.org/10.1007/978-3-030-59854-9_9

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