A First Course in Bayesian Statistical Methods

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Springer Science & Business Media, Jun 2, 2009 - Mathematics - 271 pages
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  1. A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material.

  2. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves.

  3. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.

 

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Contents

Introduction and examples
1
Belief probability and exchangeability
13
Oneparameter models
31
Monte Carlo approximation
53
The normal model
67
Posterior approximation with the Gibbs sampler
88
The multivariate normal model
105
Group comparisons and hierarchical modeling
125
Nonconjugate priors and MetropolisHastings algorithms
171
Linear and generalized linear mixed effects models
195
Latent variable methods for ordinal data
208
Exercises
225
Common distributions
252
References
259
Index
267
Copyright

Linear regression
148

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