Prune Sampling is a new MCMC method for discrete Bayesian networks that provably converges to the posterior distribution even under determinism, though it trails standard methods on large networks.
Mod- elling the effects of a cbrn defence system using a bayesian belief model
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Prune Sampling: a MCMC inference technique for discrete and deterministic Bayesian networks
Prune Sampling is a new MCMC method for discrete Bayesian networks that provably converges to the posterior distribution even under determinism, though it trails standard methods on large networks.