Markov Chain Monte Carlo Methods

Jim Albert
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Jim Albert: Bowling Green state University

Chapter 6 in Bayesian Computation with R, 2009, pp 117-152 from Springer

Abstract: In Chapter 5, we introduced the use of simulation in Bayesian inference. Rejection sampling is a general method for simulating from an arbitrary posterior distribution, but it can be difficult to set up since it requires the construction of a suitable proposal density. Importance sampling and SIR algorithms are also general-purpose algorithms, but they also require proposal densities that may be difficult to find for high-dimensional problems. In this chapter, we illustrate the use of Markov chain Monte Carlo (MCMC) algorithms in summarizing posterior distributions.

Keywords: Posterior Distribution; Markov Chain Monte Carlo; Posterior Density; Markov Chain Monte Carlo Method; Markov Chain Monte Carlo Algorithm (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1007/978-0-387-92298-0_6

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