Gilks, Walter R.; Richardson, Sylvia; Spiegelhalter, David J. Introducing Markov chain Monte Carlo. (English) Zbl 0845.60072 Gilks, W. R. (ed.) et al., Markov chain Monte Carlo in practice. London: Chapman & Hall. 1-19 (1996). This is the introductory chapter of the book, written by the three authors. A brief overview of the topic is given, including a brief introduction to Bayesian inference. The chapter then introduces the basics of Markov chain Monte Carlo (MCMC), allowing the reader to try simple applications. In particular, the authors describe the Metropolis-Hastings algorithm, the Gibbs sampler, and the main issues arising in implementing MCMC methods.For the entire collection see [Zbl 0832.00018]. Reviewer: B.H.Lindqvist (Trondheim) Cited in 2 ReviewsCited in 59 Documents MSC: 60J20 Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) 65C05 Monte Carlo methods Keywords:overview; Bayesian inference; Markov chain Monte Carlo; Metropolis-Hastings algorithm; Gibbs sampler PDFBibTeX XMLCite \textit{W. R. Gilks} et al., in: Markov chain Monte Carlo in practice. London: Chapman \& Hall. 1--19 (1996; Zbl 0845.60072)