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R by Example pp 277-305 | Cite as

Bayesian Modeling

  • Jim AlbertEmail author
  • Maria Rizzo
Chapter
Part of the Use R! book series (USE R)

Abstract

The basic tenets of Bayesian inference are introduced. This includes the construction of a prior and the use of the posterior distribution to perform inferences. Simulation is helpful in summarizing posterior distributions and Markov chain Monte Carlo algorithms are useful in simulating from posterior distributions of non-familiar forms.

Keywords

Posterior Distribution Bayesian Modeling Acceptance Rate Posterior Density 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 2012

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsBowling Green State UniversityBowling GreenUSA

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