Subjective and Objective Bayesian Statistics Principles, Models, and Applications

Name: Subjective and Objective Bayesian Statistics Principles, Models, and Applications
Price: 53.09 USD
Availability: InStock
ISBN: 9780471348436

ISBN-10: 0471348430

ISBN-13: 9780471348436

Edition: 2nd 2003 (Revised)

Authors: S. James Press, Siddhartha Chib

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Description:

Greatly expanded and revised this second edition of Bayesian Statistics includes a large number of applications to support the usefulness of the subject matter and a discussion of the rare topic of the De Finetti Transform.

Book details

List price: $194.95
Edition: 2nd
Copyright year: 2003
Publisher: John Wiley & Sons, Incorporated
Publication date: 12/9/2002
Binding: Hardcover
Pages: 608
Size: 6.24" wide x 9.55" long x 1.42" tall
Weight: 2.046
Language: English



Preface


Preface to the First Edition


A Bayesian Hall of Fame



Foundations and Principles



Background



Rationale for Bayesian Inference and Preliminary Views of Bayes' Theorem



Example: Observing a Desired Experimental Effect



Thomas Bayes



Brief Descriptions of the Chapters



A Bayesian Perspective on Probability



Introduction



Types of Probability



Coherence



Operationalizing Subjective Probability Beliefs



Calibration of Probability Assessors



Comparing Probability Definitions



The Likelihood Function



Introduction



Likelihood Function



Likelihood Principle



Likelihood Principle and Conditioning



Likelihood and Bayesian Inference



Development of the Likelihood Function Using Histograms and Other Graphical Methods



Bayes' Theorem



Introduction



General Form of Bayes' Theorem for Events



Bayes' Theorem for Discrete Data and Discrete Parameter



Bayes' Theorem for Continuous Data and Discrete Parameter



Bayes' Theorem for Discrete Data and Continuous Parameter



Bayes' Theorem for Continuous Data and Continuous Parameter



Prior Distributions



Introduction



Objective and Subjective Prior Distributions



(Univariate) Prior Distributions for a Single Parameter



Prior Distributions for Vector and Matrix Parameters



Data-Mining Priors



Wrong Priors



Numerical Implementation of the Bayesian Paradigm



Markov Chain Monte Carlo Methods




Introduction



Metropolis-Hastings (M-H) Algorithm



Multiple-Block M-H Algorithm



Some Techniques Useful in MCMC Sampling



Examples



Comparing Models Using MCMC Methods



Large Sample Posterior Distributions and Approximations



Introduction



Large-Sample Posterior Distributions



Approximate Evaluation of Bayesian Integrals



Importance Sampling



Bayesian Statistical Inference and Decision Making



Bayesian Estimation



Introduction



Univariate (Point) Bayesian Estimation



Multivariate (Point) Bayesian Estimation



Interval Estimation



Empirical Bayes' Estimation



Robustness in Bayesian Estimation



Bayesian Hypothesis Testing



Introduction



A Brief History of Scientific Hypothesis Testing



Problems with Frequentist Methods of Hypothesis Testing



Lindley's Vague Prior Procedure for Bayesian Hypothesis Testing



Jeffreys' Procedure for Bayesian Hypothesis Testing



Predictivism



Introduction



Philosophy of Predictivism



Predictive Distributions/Comparing Theories



Exchangeability



De Finetti's Theorem



The De Finetti Transform



Predictive Distributions in Classification and Spatial and Temporal Analysis



Bayesian Neural Nets



Bayesian Decision Making



Introduction



Loss Functions



Admissibility



Models and Applications



Bayesian Inference in the General Linear Model



Introduction



Simple Linear Regression



Multivariate Regression Model



Multivariate Analysis of Variance Model



Bayesian Inference in the Multivariate Mixed Model



Model Averaging




Introduction



Model Averaging and Subset Selection in Linear Regression



Prior Distributions



Posterior Distributions



Choice of Hyperparameters



Implementing BMA



Examples



Hierarchical Bayesian Modeling




Introduction



Fundamental Concepts and Nomenclature



Applications and Examples



Inference in Hierarchical Models



Relationship to Non-Bayesian Approaches



Computation for Hierarchical Models



Software for Hierarchical Models



Bayesian Factor Analysis



Introduction



Background



Bayesian Factor Analysis Model for Fixed Number of Factors



Choosing the Number of Factors



Additional Model Considerations



Bayesian Inference in Classification and Discrimination



Introduction



Likelihood Function



Prior Density



Posterior Density



Predictive Density



Posterior Classification Probability



Example: Two Populations



Second Guessing Undecided Respondents: An Application



Extensions of the Basic Classification Problem



Description of Appendices



Bayes, Thomas




Thomas Bayes. A Bibliographical Note




Communication of Bayes' Essay to the Philosophical Transactions of the Royal Society of London




An Essay Towards Solving a Problem in the Doctrine of Chances




Applications of Bayesian Statistical Science



Selecting the Bayesian Hall of Fame



Solutions to Selected Exercises


Bibliography


Subject Index


Author Index