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Accession Number:
ADP007125
Title:
Markov Chain Monte Carlo Maximum Likelihood,
Corporate Author:
MINNESOTA UNIV MINNEAPOLIS SCHOOL OF STATISTICS
Report Date:
1992-01-01
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.
Supplementary Note:
This article is from 'Computing Science and Statistics: Proceedings of the Symposium on the Interface Critical Applications of Scientific Computing: Biology, Engineering, Medicine, Speech Held in Seattle, Washington on 21-24 April 1991,' AD-A252 938, p156-163.
Pages:
0008
File Size:
0.00MB
