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• W2963417150 abstract "The paper extends the principle of cutset sampling over Bayesian networks, presented previously for Gibbs sampling, to likelihood weighting (LW). Cutset sampling is motivated by the Rao-Blackwell theorem which implies that sampling over a subset of variables requires fewer samples for convergence due to the reduction in sampling variance. The scheme exploits the network structure in selecting cutsets that allow efficient computation of the sampling distributions. In particular, as we show empirically, likelihood weighting over a loop-cutset (abbreviated LWLC), is time-wise cost-effective. We also provide an effective way for caching the probabilities of the generated samples which improves the performance of the overall scheme. We compare LWLC against regular liklihood-weighting and against Gibbs-based cutset sampling." @default.
• W2963417150 created "2019-07-30" @default.
• W2963417150 creator A5053651798 @default.
• W2963417150 creator A5066566686 @default.
• W2963417150 date "2006-07-13" @default.
• W2963417150 modified "2023-09-27" @default.
• W2963417150 title "Cutset sampling with likelihood weighting" @default.
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