Fitting More Complex Bayesian Models: Markov Chain Monte Carlo

  • Mary Kathryn Cowles
Chapter
Part of the Springer Texts in Statistics book series (STS, volume 98)

Abstract

So far we have been dealing primarily with simple, conjugate Bayesian models for which it was possible to perform exact posterior inference analytically. In more realistic and complex Bayesian models, such analytical calculations generally are not feasible. This chapter introduces the sampling-based methods of fitting Bayesian models that have transformed Bayesian statistics over the last 20 years.

Keywords

Mercury Marketing Autocorrelation Prefix Cote 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Mary Kathryn Cowles
    • 1
  1. 1.Department of Statistics and Actuarial ScienceUniversity of IowaIowa CityUSA

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