An edition of Bayesian Statistics for the Social Sciences
(2014)
Bayesian Statistics for the Social Sciences
by David Kaplan
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Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and practical skills they need to apply Bayesian methodologies to their data analysis problems. Part I addresses the elements of Bayesian inference, including exchangeability, likelihood, prior/posterior distributions, and the Bayesian central limit theorem. Part II covers Bayesian hypothesis testing, model building, and linear regression analysis, carefully explaining the differences between the Bayesian and frequentist approaches. Part III extends Bayesian statis.
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Subjects
Social sciences, statistical methods, Bayesian statistical decision theory, Social sciences, Statistical methods, PSYCHOLOGY / Statistics, MEDICAL / Nursing / Research & Theory, EDUCATION / Statistics, SOCIAL SCIENCE / Statistics, BUSINESS & ECONOMICS / Statistics, Sciences sociales, Méthodes statistiques, Théorie de la décision bayésienne, MATHEMATICS, Applied, Probability & Statistics, GeneralShowing 3 featured editions. View all 3 editions?
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1
Bayesian Statistics for the Social Sciences
2014, Guilford Publications
in English
1462516513 9781462516513
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2
Bayesian Statistics for the Social Sciences
2014, Guilford Publications
electronic resource
in English
1462516661 9781462516667
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WorldCat
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3
Bayesian Statistics for the Social Sciences
2014, Guilford Publications
in English
146251667X 9781462516674
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Libraries near you:
WorldCat
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Book Details
Table of Contents
Cover; Half Title Page; Series Page; Title Page; Copyright Page; Dedication; Series Editor's Note; Preface; Acknowledgments; Contents; PART I. FOUNDATIONS OF BAYESIAN STATISTICS; 1. Probability Concepts and Bayes' Theorem; 1.1. Relevant Probability Axioms; 1.2. Summary; 1.3. Suggested Readings; 2. Statistical Elements of Bayes' Theorem; 2.1. The Assumption of Exchangeability; 2.2. The Prior Distribution; 2.3. Likelihood; 2.4. The Posterior Distribution; 2.5. The Bayesian Central Limit Theorem and Bayesian Shrinkage; 2.6. Summary; 2.7. Suggested Readings
APPENDIX 2.1. DERIVATION OF JEFFREYS' PRIOR3. Common Probability Distributions; 3.1. The Normal Distribution; 3.2. The Uniform Distribution; 3.3. The Poisson Distribution; 3.4. The Binomial Distribution; 3.5. The Multinomial Distribution; 3.6. The Wishart Distribution; 3.7. Summary; 3.8. Suggested Readings; APPENDIX 3.1. R CODE FOR CHAPTER 3; 4. Markov Chain Monte Carlo Sampling; 4.1. Basic Ideas of MCMC Sampling; 4.2. The Metropolis-Hastings Algorithm; 4.3. The Gibbs Sampler; 4.4. Convergence Diagnostics; 4.5. Summary; 4.6. Suggested Readings; APPENDIX 4.1. R CODE FOR CHAPTER 4
PART II. TOPICS IN BAYESIAN MODELING5. Bayesian Hypothesis Testing; 5.1. Setting the Stage: The Classical Approach to Hypothesis Testing and Its Limitations; 5.2. Point Estimates of the Posterior Distribution; 5.3. Bayesian Model Evaluation and Comparison; 5.4. Bayesian Model Averaging; 5.5. Summary; 5.6. Suggested Readings; 6. Bayesian Linear and Generalized Linear Models; 6.1. A Motivating Example; 6.2. The Normal Linear Regression Model; 6.3. The Bayesian Linear Regression Model; 6.4. Bayesian Generalized Linear Models; 6.5. Summary; 6.6. Suggested Readings
APPENDIX 6.1. R CODE FOR CHAPTER 67. Missing Data from a Bayesian Perspective; 7.1. A Nomenclature for Missing Data; 7.2. Ad Hoc Deletion Methods for Handling Missing Data; 7.3. Single Imputation Methods; 7.4. Bayesian Methods of Multiple Imputation; 7.5. Summary; 7.6. Suggested Readings; APPENDIX 7.1. R CODE FOR CHAPTER 7; PART III. ADVANCED BAYESIAN MODELING METHODS; 8. Bayesian Multilevel Modeling; 8.1. Bayesian Random Effects Analysis of Variance; 8.2. Revisiting Exchangeability; 8.3. Bayesian Multilevel Regression; 8.4. Summary; 8.5. Suggested Readings; APPENDIX 8.1. R CODE FOR CHAPTER 8
9. Bayesian Modeling for Continuous and Categorical Latent Variables9.1. Bayesian Estimation of the CFA Model; 9.2. Bayesian SEM; 9.3. Bayesian Multilevel SEM; 9.4. Bayesian Growth Curve Modeling; 9.5. Bayesian Models for Categorical Latent Variables; 9.6. Summary; 9.7. Suggested Readings; APPENDIX 9.1. "rjags" CODE FOR CHAPTER 9; 10. Philosophical Debates in Bayesian Statistical Inference; 10.1. A Summary of the Bayesian versus Frequentist Schools of Statistics; 10.2. Subjective Bayes; 10.3. Objective Bayes; 10.4. Final Thoughts: A Call for Evidence-Based Subjective Bayes; References
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