This chapter describes a sequence of Monte Carlo methods: importance sampling, rejection sampling, the Metropolis method, and Gibbs sampling. For each method, we discuss whether the method is expected to be useful for high--dimensional problems such as arise in inference with graphical models. After the methods have been described, the terminology of Markov chain Monte Carlo methods is presented. The chapter concludes with a discussion of advanced methods, including methods for reducing random walk behaviour.
erice.ps.gz. | <- UK | Canada -> | erice.ps.gz.
@Incollection{MacKay97:erice, author = "D. J. C. MacKay", title = "Introduction to {M}onte {C}arlo Methods", publisher = "Kluwer Academic Press", booktitle={Learning in Graphical Models}, year = "1998", editor = "M. I. Jordan", pages = "175-204", series={NATO Science Series} }