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A First Course in Bayesian Statistical Methods

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Cover of 'A First Course in Bayesian Statistical Methods'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction and examples
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    Chapter 2 Belief, probability and exchangeability
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    Chapter 3 One-parameter models
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    Chapter 4 Monte Carlo approximation
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    Chapter 5 The normal model
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    Chapter 6 Posterior approximation with the Gibbs sampler
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    Chapter 7 The multivariate normal model
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    Chapter 8 Group comparisons and hierarchical modeling
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    Chapter 9 Linear regression
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    Chapter 10 Nonconjugate priors and Metropolis-Hastings algorithms
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    Chapter 11 Linear and generalized linear mixed effects models
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    Chapter 12 Latent variable methods for ordinal data
Attention for Chapter 10: Nonconjugate priors and Metropolis-Hastings algorithms
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