Cutset Sampling for Bayesian Networks

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 exact cutset-conditioning algorithm (Pearl, 1988). 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 network’s 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.

• Type: Journal
• Year: 2007
• Where: JAIR
• Authors: Bozhena Bidyuk, Rina Dechter
• Keywords: Bayesian Network, Exact Cutset-conditioning Algorithm, JAIR 2007, Memory-based Inference Algorithms