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Please use this identifier to cite or link to this item: http://hdl.handle.net/11299/58440

Title: Markov Chain Monte Carlo Maximum Likelihood
Authors: Geyer, Charles J.
Issue Date: 1991
Publisher: Interface Foundation of North America
Citation: Computing Science and Statistics, Proceedings of the 23rd Symposium on the Interface, pp. 156-163
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.
URI: http://purl.umn.edu/58440
Appears in Collections:Dr. Charles Geyer

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