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We introduce and characterise the performance of the Markov chain Monte Carlo(MCMC) inference method Prune Sampling for discrete and deterministic Bayesiannetworks (BNs). We developed a procedure to obtain the performance of a MCMCsampling method in the limit of infinite simulation time, extrapolated fromrelatively short simulations. This approach was used to conduct a study tocompare the accuracy, rate of convergence and the time consumption of PruneSampling with two conventional MCMC sampling methods: Gibbs- and Metropolissampling. We show that Markov chains created by Prune Sampling always convergeto the desired posterior distribution, also for networks where conventionalGibbs sampling fails. Beside this, we demonstrate that pruning outperformsGibbs sampling, at least for a certain class of BNs. Though, this temptingfeature comes at a price. In the first version of Prune Sampling, for large BNsthe procedure to choose the next iteration step uniformly is rather timeintensive. Our conclusion is that Prune Sampling is a competitive method forall types of small and medium sized BNs, but (for now) standard methods stillperform better for all types of large BNs.

Prune Sampling: a MCMC inference technique for discrete and deterministic Bayesian networks