Title Subjective and objective Bayesian statistics : principles, models, and applications / S. James Press ; with contributions by Siddhartha Chib ... [and others]

Edition Second edition Published Hoboken, N.J. ; [Chichester] : Wiley-Interscience, [2003] ©2003

Copies

Location	Call no.	Vol.	Availability
W'PONDS	519.542 Pre/Sao 2003		AVAILABLE

Description xxx, 558 pages : illustrations, portraits ; 25 cm Series Wiley series in probability and statistics Wiley series in probability and statistics. Contents Machine derived contents note: Preface. -- Preface to the First Edition. -- A Bayesian Hall of Fame. -- Part I: Foundations And Principles. -- 1. Background. -- 1.1 Rationale for Bayesian Inference and Preliminary Views of Bayes Theorem. -- 1.2 Example: Observing a Desired Experimental Effect. -- 1.3 Thomas Bayes. -- 1.4 Brief Descriptions of the Chapters. -- 2. A Bayesian Perspective on Probability. -- 2.1 Introduction. -- 2.2 Types of Probability. -- 2.3 Coherence. -- 2.4 Operationalizing Subjective Probability Beliefs. -- 2.5 Calibration of Probability Assessors. -- 2.6 Comparing Probability Definitions. -- 3. The Likelihood Function. -- 3.1 Introduction. -- 3.2 Likelihood Function. -- 3.3 Likelihood Principle. -- 3.4 Likelihood Principle and Conditioning. -- 3.5 Likelihood and Bayesian Inference. -- 3.6 Development of the Likelihood Function Using Histograms and Other Graphical Methods. -- 4. Bayes Theorem. -- 4.1 Introduction. -- 4.2 General Form of Bayes Theorem for Events. -- 4.3 Bayes Theorem for Discrete Data and Discrete Parameter. -- 4.4 Bayes Theorem for Continuous Data and Discrete Parameter. -- 4.5 Bayes Theorem for Discrete Data and Continuous Parameter. -- 4.6 Bayes Theorem for Continuous Data and Continuous Parameter. -- 5. Prior Distributions. -- 5.1 Introduction. -- 5.2 Objective and Subjective Prior Distributions. -- 5.3 (Univariate) Prior Distributions for a Single Parameter. -- 5.4 Prior Distributions for Vector and Matrix Parameters. -- 5.5 Data-Mining Priors. -- 5.6 Wrong Priors. -- Part Ii: Numerical Implementation Of The Bayesian Paradigm. -- 6. Markov Chain Monte Carlo Methods (Siddhartha Chib). -- 6.1 Introduction. -- 6.2 Metropolis?Hastings (M?H) Algorithm. -- 6.3 Multiple-Block M?H Algorithm. -- 6.4 Some Techniques Useful in MCMC Sampling. -- 6.5 Examples. -- 6.6 Comparing Models Using MCMC Methods. -- 7. Large Sample Posterior Distributions and Approximations. -- 7.1 Introduction. -- 7.2 Large-Sample Posterior Distributions. -- 7.3 Approximate Evaluation of Bayesian Integrals. -- 7.4 Importance Sampling. -- Part Iii: Bayesian Statistical Inference And Decision Making. -- 8. Bayesian Estimation. -- 8.1 Introduction. -- 8.2 Univariate (Point) Bayesian Estimation. -- 8.3 Multivariate (Point) Bayesian Estimation. -- 8.4 Interval Estimation. -- 8.5 Empirical Bayes Estimation. -- 8.6 Robustness in Bayesian Estimation. -- 9. Bayesian Hypothesis Testing. -- 9.1 Introduction. -- 9.2 A Brief History of Scientific Hypothesis Testing. -- 9.3 Problems with Frequentist Methods of Hypothesis Testing. -- 9.4 Lindleys Vague Prior Procedure for Bayesian Hypothesis Testing. -- 9.5 Jeffreys Procedure for Bayesian Hypothesis Testing. -- 10. Predictivism. -- 10.1 Introduction. -- 10.2 Philosophy of Predictivism. -- 10.3 Predictive Distributions=Comparing Theories. -- 10.4 Exchangeability. -- 10.5 De Finettis Theorem. -- 10.6 The De Finetti Transform. -- 10.7 Predictive Distributions in Classification and Spatial and Temporal -- Analysis. -- 10.8 Bayesian Neural Nets. -- 11. Bayesian Decision Making. -- 11.1 Introduction. -- 11.2 Loss Functions. -- 11.3 Admissibility. -- Part Iv: Models And Applications. -- 12. Bayesian Inference in the General Linear Model. -- 12.1 Introduction. -- 12.2 Simple Linear Regression. -- 12.3 Multivariate Regression Model. -- 12.4 Multivariate Analysis of Variance Model. -- 12.5 Bayesian Inference in the Multivariate Mixed Model. -- 13. Model Averaging (Merlise Clyde). -- 13.1 Introduction. -- 13.2 Model Averaging and Subset Selection in Linear Regression. -- 13.3 Prior Distributions. -- 13.4 Posterior Distributions. -- 13.5 Choice of Hyperparameters. -- 13.6 Implementing BMA. -- 13.7 Examples. -- 14. Hierarchical Bayesian Modeling (Alan Zaslavsky). -- 14.1 Introduction. -- 14.2 Fundamental Concepts and Nomenclature. -- 14.3 Applications and Examples. -- 14.4 Inference in Hierarchical Models. -- 14.5 Relationship to Non-Bayesian Approaches. -- 14.6 Computation for Hierarchical Models. -- 14.7 Software for Hierarchical Models. -- 15. Bayesian Factor Analysis. -- 15.1 Introduction. -- 15.2 Background. -- 15.3 Bayesian Factor Analysis Model for Fixed Number of Factors. -- 15.4 Choosing the Number of Factors. -- 15.5 Additional Model Considerations. -- 16. Bayesian Inference in Classification and Discrimination. -- 16.1 Introduction. -- 16.2 Likelihood Function. -- 16.3 Prior Density. -- 16.4 Posterior Density. -- 16.5 Predictive Density. -- 16.6 Posterior Classification Probability. -- 16.7 Example: Two Populations. -- 16.8 Second Guessing Undecided Respondents: An Application. -- 16.9 Extensions of the Basic Classification Problem. -- Description of Appendices. -- Appendix 1. Bayes, Thomas, (Hilary L. Seal). -- Appendix 2. Thomas Bayes. A Bibliographical Note (George A. Barnard). -- Appendix 3. Communication of Bayes Essay to the Philosophical Transactions of the Royal Society of London (Richard Price). -- Appendix 4. An Essay Towards Solving a Problem in the Doctrine of Chances (Reverend Thomas Bayes). -- Appendix 5. Applications of Bayesian Statistical Science. -- Appendix 6. Selecting the Bayesian Hall of Fame. -- Appendix 7. Solutions to Selected Exercises. -- Bibliography. -- Subject Index. -- Author Index Notes Previous ed.: 1989 Bibliography Includes bibliographical references and index Subject Bayesian statistical decision theory. LC no. 2003266148 ISBN 0471348430

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