Symbolic Model Checking for Factored Probabilistic Models

Deininger, David D.; Dimitrova, Rayna; Majumdar, Rupak

doi:10.1007/978-3-319-46520-3_28

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…Factored state spaces are often represented as (bipartite) Dynamic Bayesian networks. ADD-based model checking for DBNs has been investigated in [25], with mixed results. Their investigation focuses on using ADDs for factored state space representations.…”

Section: Discussion Related Work and Conclusionmentioning

confidence: 99%

Model Checking Finite-Horizon Markov Chains with Probabilistic Inference

Holtzen

Junges

Vazquez-Chanlatte

et al. 2021

Lecture Notes in Computer Science

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We revisit the symbolic verification of Markov chains with respect to finite horizon reachability properties. The prevalent approach iteratively computes step-bounded state reachability probabilities. By contrast, recent advances in probabilistic inference suggest symbolically representing all horizon-length paths through the Markov chain. We ask whether this perspective advances the state-of-the-art in probabilistic model checking. First, we formally describe both approaches in order to highlight their key differences. Then, using these insights we develop Rubicon, a tool that transpiles Prism models to the probabilistic inference tool . Finally, we demonstrate better scalability compared to probabilistic model checkers on selected benchmarks. All together, our results suggest that probabilistic inference is a valuable addition to the probabilistic model checking portfolio, with Rubicon as a first step towards integrating both perspectives.

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Section: Discussion Related Work and Conclusionmentioning

confidence: 99%

Model Checking Finite-Horizon Markov Chains with Probabilistic Inference

Holtzen

Junges

Vazquez-Chanlatte

et al. 2021

Lecture Notes in Computer Science

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show abstract

“…We here concentrate on the most relevant papers. Deininger et al [21] is perhaps the closest related work. They apply PCTL model checking on factored representations of dynamic BNs and compare an MTBDD-based approach using partitioned representations of the transition probability matrix to monolithic representations.…”

Section: Related Workmentioning

confidence: 99%

Bayesian Inference by Symbolic Model Checking

Salmani

Katoen

2020

Quantitative Evaluation of Systems

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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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“…Factored state spaces are often represented as (bipartite) Dynamic Bayesian networks. ADD-based model checking for DBNs has been investigated in [27], with mixed results. Their investigation focuses on using ADDs for factored state space representations.…”

Section: Related Workmentioning

confidence: 99%

Model Checking Finite-Horizon Markov Chains with Probabilistic Inference

Holtzen¹,

Junges²,

Vazquez-Chanlatte³

et al. 2021

Preprint

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We revisit the symbolic verification of Markov chains with respect to finite horizon reachability properties. The prevalent approach iteratively computes step-bounded state reachability probabilities. By contrast, recent advances in probabilistic inference suggest symbolically representing all horizon-length paths through the Markov chain. We ask whether this perspective advances the state-of-the-art in probabilistic model checking. First, we formally describe both approaches in order to highlight their key differences. Then, using these insights we develop Rubicon, a tool that transpiles Prism models to the probabilistic inference tool Dice. Finally, we demonstrate better scalability compared to probabilistic model checkers on selected benchmarks. All together, our results suggest that probabilistic inference is a valuable addition to the probabilistic model checking portfolio -with Rubicon as a first step towards integrating both perspectives.

show abstract

Symbolic Model Checking for Factored Probabilistic Models

Cited by 4 publications

References 12 publications

Model Checking Finite-Horizon Markov Chains with Probabilistic Inference

Model Checking Finite-Horizon Markov Chains with Probabilistic Inference

Bayesian Inference by Symbolic Model Checking

Model Checking Finite-Horizon Markov Chains with Probabilistic Inference

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