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A Model Counter's Guide to Probabilistic Systems

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arxiv 1903.09354 v1 pith:6RQ3L5QU submitted 2019-03-22 cs.LO cs.AI

Marcell Vazquez-Chanlatte , Markus N. Rabe , Sanjit A. Seshia This is my paper · ORCID
classification cs.LOcs.AI
keywords modelprobabilisticsystemscoinscountingmodelinganalyzingbiased
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In this paper, we systematize the modeling of probabilistic systems for the purpose of analyzing them with model counting techniques. Starting from unbiased coin flips, we show how to model biased coins, correlated coins, and distributions over finite sets. From there, we continue with modeling sequential systems, such as Markov chains, and revisit the relationship between weighted and unweighted model counting. Thereby, this work provides a conceptual framework for deriving #SAT encodings for probabilistic inference.

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  1. Towards Unified Probabilistic Verification and Validation of Vision-Based Autonomy

    eess.SY 2025-08 reject novelty 5.0 of 10

    A pipeline that turns collected runs of a vision-based controller into an interval MDP, verifies a safety lower bound, and reuses Bayesian conformance to extend the bound to new environments.

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