Cutset Sampling for Bayesian Networks

Cutset sampling for Bayesian networks 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 appro...

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 appro...

Cycle-Cutset Sampling for Bayesian Networks

An Empirical Study of w-Cutset Sampling for Bayesian Networks

The paper studies empirically the time-space trade-off between sampling and inference in the cutset sampling algorithm. The algorithm samples over a subset of nodes in a Bayesian network and applies exact inference over the rest. As the size of the sampling space decreases, requiring less samples for convergence, the time for generating each single sample increases. Algorithm wcutset sampling s...

cutset effect in Bayesian networks

The paper investigates the behavior of iterative belief propagation algorithm (IBP) in Bayesian networks with loops. In multiply-connected network, IBP is only guaranteed to converge in linear time to the correct posterior marginals when evidence nodes form a loop-cutset. We propose an -cutset criteria that IBP will converge and compute posterior marginals close to correct when a single value i...