DocumentCode
2342127
Title
Markov chain Monte Carlo algorithms
Author
Rosenthal, Jeffrey S.
Author_Institution
Dept. of Stat., Toronto Univ., Ont., Canada
fYear
1994
fDate
27-29 Oct 1994
Firstpage
46
Abstract
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
Keywords
Markov processes; Monte Carlo methods; convergence of numerical methods; iterative methods; probability; signal sampling; statistical analysis; Gibbs sampler; Markov chain Monte Carlo algorithms; Metropolis-Hastings algorithm; algorithm convergence; iterations bounds; probability distributions; statistics; Bayesian methods; Convergence; Image sampling; Inference algorithms; Internet; Monte Carlo methods; Probability distribution; Statistical distributions; Statistics; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on
Conference_Location
Alexandria, VA
Print_ISBN
0-7803-2761-6
Type
conf
DOI
10.1109/WITS.1994.513879
Filename
513879
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