  We introduce and characterise the performance of the Markov chain Monte Carlo
(MCMC) inference method Prune Sampling for discrete and deterministic Bayesian
networks (BNs). We developed a procedure to obtain the performance of a MCMC
sampling method in the limit of infinite simulation time, extrapolated from
relatively short simulations. This approach was used to conduct a study to
compare the accuracy, rate of convergence and the time consumption of Prune
Sampling with two conventional MCMC sampling methods: Gibbs- and Metropolis
sampling. We show that Markov chains created by Prune Sampling always converge
to the desired posterior distribution, also for networks where conventional
Gibbs sampling fails. Beside this, we demonstrate that pruning outperforms
Gibbs sampling, at least for a certain class of BNs. Though, this tempting
feature comes at a price. In the first version of Prune Sampling, for large BNs
the procedure to choose the next iteration step uniformly is rather time
intensive. Our conclusion is that Prune Sampling is a competitive method for
all types of small and medium sized BNs, but (for now) standard methods still
perform better for all types of large BNs.
