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Erschienen in:
1996 | OriginalPaper | Buchkapitel
Markov Chain Monte Carlo Methods
verfasst von : Joseph J. K. Ó Ruanaidh, William J. Fitzgerald
Erschienen in: Numerical Bayesian Methods Applied to Signal Processing
Verlag: Springer New York
Enthalten in: Professional Book Archive
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Sampling methods based on Markov chains were first developed for applications in statistical physics. Two branches of development originated in the 1950s. The classic paper by Metropolis et al [77] introduced what is now known as the Metropolis algorithm. This method was popularized for Bayesian applications, along with its variant the Gibbs sampler, by the influential papers of Geman and Geman [36], who applied it to image processing, and Gelfand and Smith [35], who demonstrated its application to Bayesian problems in general.