内容简介：Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in arti_cial intelligence. Computational di_culties arise, however,because probabilistic models with the necessary realism and exibility lead to complexdistributions over high-dimensional spaces.

Related problems in other fields have been tackled using Monte Carlo methods based on sampling using Markov chains, providing a rich array of techniques that can be applied to problems in arti_cial intelligence. The \Metropolis algorithm" has been used to solve di_cult problems in statistical physics for over forty years, and, in thelast few years, the related method of \Gibbs sampling" has been applied to problems of statistical inference. Concurrently, an alternative method for solving problems
in statistical physics by means of dynamical simulation has been developed as well,and has recently been uni_ed with the Metropolis algorithm to produce the \hybridMonte Carlo" method. In computer science, Markov chain sampling is the basisof the heuristic optimization technique of \simulated annealing", and has recentlybeen used in randomized algorithms for approximate counting of large sets.

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