Abstract
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.
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© 1996 Springer Science+Business Media New York
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Ó Ruanaidh, J.J.K., Fitzgerald, W.J. (1996). Markov Chain Monte Carlo Methods. In: Numerical Bayesian Methods Applied to Signal Processing. Statistics and Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0717-7_4
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DOI: https://doi.org/10.1007/978-1-4612-0717-7_4
Publisher Name: Springer, New York, NY
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