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
Computing Science and Statistics, Proceedings of the 23rd Symposium on the Interface, pp. 156-163
Geyer, Charles J..
Markov Chain Monte Carlo Maximum Likelihood.
Interface Foundation of North America.
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