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Using Gibbs Samplers to Compute Bayesian Posterior Distributions

  • Eric A. Suess
  • Bruce E. Trumbo
Chapter
Part of the Use R book series (USE R, volume 0)

Abstract

In Chapter 8, we introduced the fundamental ideas of Bayesian inference, in which prior distributions on parameters are used together with data to obtain posterior distributions and thus interval estimates of parameters. However, in practice, Bayesian posterior distributions are often difficult to compute.

Keywords

Posterior Distribution Prior Distribution Gibbs Sampler Bayesian Estimate Interval Estimate 
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, LLC 2010

Authors and Affiliations

  1. 1.Department of Statistics and BiostatisticsCalifornia State University, East BayHaywardUSA

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