Title :
Markov chain Monte Carlo algorithms
Author :
Rosenthal, Jeffrey S.
Author_Institution :
Dept. of Stat., Toronto Univ., Ont., Canada
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;
Conference_Titel :
Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on
Conference_Location :
Alexandria, VA
Print_ISBN :
0-7803-2761-6
DOI :
10.1109/WITS.1994.513879
