Accession Number : ADP007125

Title :   Markov Chain Monte Carlo Maximum Likelihood,

Corporate Author : MINNESOTA UNIV MINNEAPOLIS SCHOOL OF STATISTICS

Personal Author(s) : Geyer, Charles J.

Report Date : 1992

Pagination or Media Count : 8

Abstract : Markov chain Monte Carlo (e. g., the Metropolis algorithm and Gibbs sampler) is a general tool for simulation of complex stochastic processes useful in many types of statistical inference. The basics of Markov chain Monte Carlo are reviewed, including choice of algorithms and variance estimation, and some new methods are introduced. The use of Markov chain Monte Carlo for maximum likelihood estimation is explained, and its performance is compared with maximum pseudo likelihood estimation. Markov chain, Monte Carlo, Maximum likelihood, Metropolis algorithm, Gibbs sampler, Variance estimation.

Descriptors :   *MAXIMUM LIKELIHOOD ESTIMATION, *STOCHASTIC PROCESSES, ALGORITHMS, CHAINS, PROBABILITY, SAMPLERS, SELECTION, SIMULATION, STATISTICAL INFERENCE, TOOLS.

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE