| 청구기호 |
HA29 .K344 2014 |
| 형태사항 |
1 online resource (338 p.)
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| 언어 |
English |
| 일반주기 |
Description based upon print version of record.
Author Index
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| 내용 |
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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| 주제 |
Bayesian statistical decision theory.
BUSINESS & ECONOMICS / Statistics.
EDUCATION / Statistics.
MEDICAL / Nursing / Research & Theory.
PSYCHOLOGY / Statistics.
SOCIAL SCIENCE / Statistics.
Social sciences -- Statistical methods.
Social sciences --Statistical methods.
Bayesian statistical decision theory.
MATHEMATICS / Applied --bisacsh
MATHEMATICS / Probability & Statistics / General --bisacsh
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| ISBN |
9781462516667 (electronic bk.)
1462516661 (electronic bk.)
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