Markov chain Monte Carlo methods: computation and inference

Siddhartha Chib

Chapter 57 in Handbook of Econometrics, 2001, vol. 5, pp 3569-3649 from Elsevier

Abstract: This chapter reviews the recent developments in Markov chain Monte Carlo simulation methods. These methods, which are concerned with the simulation of high dimensional probability distributions, have gained enormous prominence and revolutionized Bayesian statistics. The chapter provides background on the relevant Markov chain theory and provides detailed information on the theory and practice of Markov chain sampling based on the Metropolis-Hastings and Gibbs sampling algorithms. Convergence diagnostics and strategies for implementation are also discussed. A number of examples drawn from Bayesian statistics are used to illustrate the ideas. The chapter also covers in detail the application of MCMC methods to the problems of prediction and model choice.

JEL-codes: C39 (search for similar items in EconPapers)
Date: 2001
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Citations: View citations in EconPapers (259)

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