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
Geyer, Charles J..
Markov Chain Monte Carlo Maximum Likelihood.
Interface Foundation of North America.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital
Conservancy may be subject to additional license and use
restrictions applied by the depositor.