Parallel Probabilistic Inference by Weighted Model Counting

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Giso H. Dal, Alfons W. Laarman, Peter J.F. Lucas ;
Proceedings of the Ninth International Conference on Probabilistic Graphical Models, PMLR 72:97-108, 2018.

Abstract

Knowledge compilation as part of the Weighted Model Counting approach has proven to be an efficient tool for exact inference in probabilistic graphical models, by exploiting structures that more traditional methods can not. The availability of affordable high performance commodity hardware has been an inspiration for other inference approaches to exploit parallelism, to great suc- cess. In this paper, we explore the possibilities for Weighted Model Counting. We have empirically confirmed that exploited parallelism yields substantial speedups using a set of real-world Bayesian networks.

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