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

Title 
Markov Chain Monte Carlo Maximum Likelihood

Author(s)

Issue Date
1991

Publisher
Interface Foundation of North America

Type
Conference Paper

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.

Appears in Collection(s)

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, http://purl.umn.edu/58440.


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