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Markov Chain Monte Carlo Methods

  • Jim Albert
Chapter

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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York 2009

Authors and Affiliations

  • Jim Albert
    • 1
  1. 1.Bowling Green state UniversityBowling GreenUSA

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