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Markov chain Monte Carlo algorithms

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1 Author(s)
Rosenthal, J.S. ; Dept. of Stat., Toronto Univ., Ont., Canada

We briefly describe Markov chain Monte Carlo algorithms, such as the Gibbs sampler and the Metropolis-Hastings (1953, 1970) algorithm, which are frequently used in the statistics literature to explore complicated probability distributions. We present a general method for proving rigorous, a priori bounds an the number of iterations required to achieve convergence of the algorithms

Published in:

Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on

Date of Conference:

27-29 Oct 1994