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Simulation Methods

  • Larry Wasserman
Part of the Springer Texts in Statistics book series (STS)

Abstract

In this chapter we will show how simulation can be used to approximate integrals. Our leading example is the problem of computing integrals in Bayesian inference but the techniques are widely applicable. We will look at three integration methods: (i) basic Monte Carlo integration, (ii) importance sampling, and (iii) Markov chain Monte Carlo (MCMC).

Keywords

Markov Chain Markov Chain Monte Carlo Bayesian Inference Gibbs Sampling Importance Sampling 
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 Science+Business Media New York 2004

Authors and Affiliations

  • Larry Wasserman
    • 1
  1. 1.Department of StatisticsCarnegie Mellon UniversityPittsburghUSA

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