Publication
Exploiting Causal Independence Using Weighted Model Counting
AbstractPrevious studies have demonstrated that encoding a Bayesian network into a SAT-CNF formula and then performing weighted model counting using a backtracking search algorithm can be an effective method for exact inference in Bayesian networks. In this paper, we present techniques for improving this approach for Bayesian networks with noisy-OR and noisy-MAX relations—two relations which are widely used in practice as they can dramatically reduce thenumber of probabilities one needs to specify. In particular, we present two space efficient CNF encodings for noisy-OR/MAX and explore alternative search ordering heuristics. We experimentally evaluated our techniques on large-scale real and randomly generated Bayesian networks. On these benchmarks, our techniques gave speedups of up to two ordersof magnitude over the best previous approaches and scaled up to networks with larger numbers of random variables.
Download publicationRelated Resources
See what’s new.
2020
MicroMentor: Peer-to-Peer Software Help Sessions in Three Minutes or LessWhile synchronous one-on-one help for software learning is rich and…
2022
BeNTO: Beam Network Topology OptimizationWe present an optimization framework that allows designers and…
2017
Bio Computational Evolution: The Next Generation of Software for Synthetic BiologyPioneering the intersection between synthetic biology, architecture,…
Get in touch
Something pique your interest? Get in touch if you’d like to learn more about Autodesk Research, our projects, people, and potential collaboration opportunities.
Contact us