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

• Record Nr.: UNINA9911018921203321
• Autore: Press S. James
• Pubbl/distr/stampa: Hoboken, N.J., : Wiley-Interscience, c2003
• ISBN: 9786612307669; 9781282307667; 1282307665; 9780470317105; 0470317108; 9780470317945; 0470317949
• Edizione: [2nd ed.]
• Descrizione fisica: 1 online resource (591 p.)
• Collana: Wiley series in probability and statistics
• Disciplina: 519.5/42
• Soggetti: Bayesian statistical decision theory
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Subjective and Objective Bayesian Statistics Principles, Models, and Applications; CONTENTS; 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 Prelirmnary Views of Bayes' Theorem; 1.2 Example: Observing a Desired Experimental Effect; 1.3 Thomas Bayes; 1.4 Brief Descriptions of the Chapters; summary; Exercises; Further Reading; 2. A Bayesian Perspective on Probability; 2.1 Introduction; 2.2 Types of Probability; 2.2.1 Axiom Systems; 2.2.2 Frequency and Long-Run Probability; 2.2.3 Logical Probability2.2.4 Kolmogorov Axiom System of Frequency Probability; 2.2.5 Savage System of Axioms of Subjective Probability; 2.2.6 Rényi Axiom System of Probability; 2.3 Coherence; 2.3.1 Example of Incoherence; 2.4 Operationalizing Subjective Probability Beliefs; 2.4.1 Example of Subjective Probability Definition and Operationalization; 2.5 Calibration of Probability Assessors; 2.6 Comparing Probability Definitions; Summary; Complement to Chapter 2 ; The Axiomatic Foundation of Decision making of L. J. Savage; Utility Functions; Exercises; Further Reading; 3. The Likelihood Function3.1 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; summary; Exercises; Further Reading; 4. Bayeds' Theorem; 4.1 Introduction; 4.2 General Form of Bayes' Theorem for Events; 4.2.1 Bayes' Theorem for Complementary Events; 4.2.2 Prior Probabilities; 4.2.3 Posterior Probabilities; 4.2.4 Odds Ratios; Example 4.1 Bayes' Theorem for Events: DNA Fingerprinting; 4.3 Bayes' Theorem for Discrete Data and Discrete Parameter4.3.1 Interpretation of Bayes' Theorem for Discrete Data and Discrete Parameter; Example 4.2 Quality Control in Manufacturing: Discrete Data and Discrete Parameter (Inference About a Proportion); 4.3.2 Bayes' Theorem for Discrete Data and Discrete Models; 4.4 Bayes' Theorem for Continuous Data and Discrete Parameter; 4.4.1 Interpretation of Bayes' Theorem for Continuous Data and Discrete Parameter; Example 4.3 Infming the Section of a Class from which Student was Selected: Continuous Data and Discrcte Parameter (Choosing from a Discrete Set of Models)4.5 Bayes' Theorem for Discrete Data and Continuous Parameter; Example 4.4 Quality Control in Manufacturing: Discrete Data and Continuous Parameter; 4.6 Baycs' Theorem for Continuous Data and Continuous Parameter; Example 4.5 Normal Data: Unknown Mean, Known Variance; Example 4.6 Normal Data: Unknown Mean, Unknown Variance; Summary; Exercises; Further Reading; Complement to Chapter 4: Heights of the Standard Normal Density; 5. Prior Distributions
• Sommario/riassunto: Shorter, more concise chapters provide flexible coverage of the subject.Expanded coverage includes: uncertainty and randomness, prior distributions, predictivism, estimation, analysis of variance, and classification and imaging.Includes topics not covered in other books, such as the de Finetti Transform.Author S. James Press is the modern guru of Bayesian statistics.