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AAAI
2008

Exploiting Causal Independence Using Weighted Model Counting

10 years 3 months ago
Exploiting Causal Independence Using Weighted Model Counting
Previous 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 the number of probabilities one needs to specify. In particular, we present two space efficient CNF encodings for noisyOR/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 orders of magnitude over the best previous approaches and scaled up to networks with larger numbers of random variables.
Wei Li 0002, Pascal Poupart, Peter van Beek
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2008
Where AAAI
Authors Wei Li 0002, Pascal Poupart, Peter van Beek
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