Subjective and Objective Bayesian Statistics Principles, Models, and Applications

ISBN-10: 0471348430
ISBN-13: 9780471348436
Edition: 2nd 2003 (Revised)
List price: $182.00 Buy it from $137.96
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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.

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Book details

List price: $182.00
Edition: 2nd
Copyright year: 2003
Publisher: John Wiley & Sons, Incorporated
Publication date: 12/9/2002
Binding: Hardcover
Pages: 600
Size: 6.25" wide x 9.50" long x 1.50" tall
Weight: 2.046
Language: English

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.

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

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