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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

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Cover of 'Introduction to Applied Bayesian Statistics and Estimation for Social Scientists'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Probability Theory and Classical Statistics
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    Chapter 3 Basics of Bayesian Statistics
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    Chapter 4 Modern Model Estimation Part 1: Gibbs Sampling
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    Chapter 5 Modern Model Estimation Part 2: Metroplis–Hastings Sampling
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    Chapter 6 Evaluating Markov Chain Monte Carlo Algorithms and Model Fit
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    Chapter 7 The Linear Regression Model
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    Chapter 8 Generalized Linear Models
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    Chapter 9 Introduction to Hierarchical Models
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    Chapter 10 Introduction to Multivariate Regression Models
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    Chapter 11 Conclusion
Attention for Chapter 6: Evaluating Markov Chain Monte Carlo Algorithms and Model Fit
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