Markov Chain Monte Carlo Maximum Likelihood

Geyer, Charles J.
1991
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Markov Chain Monte Carlo Maximum Likelihood

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1991

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Interface Foundation of North America

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Conference Paper

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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.

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Dr. Charles J. Geyer

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

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Geyer, Charles J.. (1991). Markov Chain Monte Carlo Maximum Likelihood. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/58440.

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