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Markov Chain Monte Carlo Maximum Likelihood
Geyer, Charles J. (Interface Foundation of North America, 1991)

Markov Chain Monte Carlo Maximum Likelihood


Issue Date

Interface Foundation of North America

Conference Paper

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.

Appears in Collection(s)

Previously Published Citation
Computing Science and Statistics, Proceedings of the 23rd Symposium on the Interface, pp. 156-163

Suggested Citation
Geyer, Charles J.. (1991). Markov Chain Monte Carlo Maximum Likelihood. Interface Foundation of North America. Retrieved from the University of Minnesota Digital Conservancy,

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