A Model Counter's Guide to Probabilistic Systems

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/15/2020

Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry

We study the symmetric weighted first-order model counting task and pres...
research
11/20/2014

Stable Model Counting and Its Application in Probabilistic Logic Programming

Model counting is the problem of computing the number of models that sat...
research
02/04/2021

On Stochastic Rewriting and Combinatorics via Rule-Algebraic Methods

Building upon the rule-algebraic stochastic mechanics framework, we pres...
research
10/31/2011

Probabilistic Planning via Heuristic Forward Search and Weighted Model Counting

We present a new algorithm for probabilistic planning with no observabil...
research
01/16/2014

Exploiting Structure in Weighted Model Counting Approaches to Probabilistic Inference

Previous studies have demonstrated that encoding a Bayesian network into...
research
10/31/2016

Edward: A library for probabilistic modeling, inference, and criticism

Probabilistic modeling is a powerful approach for analyzing empirical in...
research
04/05/2018

Incremental Verification of Parametric and Reconfigurable Markov Chains

The analysis of parametrised systems is a growing field in verification,...

Please sign up or login with your details

Forgot password? Click here to reset