A Short History of Markov Chain Monte Carlo: Subjective Recollections from Incomplete Data

We attempt to trace the history and development of Markov chain Monte Carlo (MCMC) from its early inception in the late 1940s through its use today. We see how the earlier stages of Monte Carlo (MC, not MCMC) research have led to the algorithms currently in use. More importantly, we see how the deve...

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Bibliographic Details
Published in:Statistical science 2011-02, Vol.26 (1), p.102-115
Main Authors: Robert, Christian, Casella, George
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Bayesian methods
Ergodic theory
Gibbs sampling
hierarchical models
Markov analysis
Markov chains
Metropolis–Hasting algorithm
Metropolitan areas
Modeling
Monte Carlo methods
Monte Carlo simulation
Physics
Probability
Sampling distributions
Statistical methods
Tanneries
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Summary:We attempt to trace the history and development of Markov chain Monte Carlo (MCMC) from its early inception in the late 1940s through its use today. We see how the earlier stages of Monte Carlo (MC, not MCMC) research have led to the algorithms currently in use. More importantly, we see how the development of this methodology has not only changed our solutions to problems, but has changed the way we think about problems.
ISSN:0883-4237
2168-8745
DOI:10.1214/10-sts351