Cutset Sampling for Bayesian Networks

Details Cutset Sampling for Bayesian Networks Cutset Sampling for Bayesian Networks

2011-10-12

arxiv.org/abs/1110.2740v1

Journal Of Artificial Intelligence Research, Volume 28, pages 1-48, 2007

Computer Science

Artificial Intelligence

Scientific paper

10.1613/jair.2149

The paper presents a new sampling methodology for Bayesian networks that samples only a subset of variables and applies exact inference to the rest. Cutset sampling is a network structure-exploiting application of the Rao-Blackwellisation principle to sampling in Bayesian networks. It improves convergence by exploiting memory-based inference algorithms. It can also be viewed as an anytime approximation of the exact cutset-conditioning algorithm developed by Pearl. Cutset sampling can be implemented efficiently when the sampled variables constitute a loop-cutset of the Bayesian network and, more generally, when the induced width of the networks graph conditioned on the observed sampled variables is bounded by a constant w. We demonstrate empirically the benefit of this scheme on a range of benchmarks.

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