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- W4283726152 abstract "The subject of Markov Chain Monte Carlo (MCMC) is considered in this chapter. At its core, MCMC is a clever way to obtain samples from almost any arbitrary distributions and, as such, it could have been included in Chapter 4. However, MCMC deserves its own chapter because it has such breadth of application. Whereas its main feature is that it allows highly complex and multidimensional distributions to be sampled, it also applies to simpler cases. We consider Markov Chains (on which MCMC is based), the Metropolis–Hastings Algorithm (the most popular implementation of MCMC), Bayes' theorem (and some of its implications), and the use of MCMC in inference and decision analysis. Concepts such as “burn-in”, prior and posterior distributions, Gibbs sampling, and conjugate and nonconjugate distributions are also covered. Eight example problems are given, in order to illustrate how MCMC can be implemented." @default.
- W4283726152 created "2022-07-01" @default.
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- W4283726152 date "2023-01-01" @default.
- W4283726152 modified "2023-09-25" @default.
- W4283726152 title "Markov Chain Monte Carlo" @default.
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- W4283726152 doi "https://doi.org/10.1016/b978-0-12-819739-4.00014-7" @default.
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