LEADER 05021nam 2200625 a 450 001 9910830030103321 005 20170810175144.0 010 $a1-282-30766-5 010 $a9786612307669 010 $a0-470-31710-8 010 $a0-470-31794-9 035 $a(CKB)1000000000807596 035 $a(EBL)469810 035 $a(OCoLC)587388980 035 $a(SSID)ssj0000343357 035 $a(PQKBManifestationID)11231094 035 $a(PQKBTitleCode)TC0000343357 035 $a(PQKBWorkID)10290932 035 $a(PQKB)10626247 035 $a(MiAaPQ)EBC469810 035 $a(PPN)159302277 035 $a(EXLCZ)991000000000807596 100 $a20030313d2003 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aSubjective and objective Bayesian statistics$b[electronic resource] $eprinciples, models, and applications /$fS. James Press ; with contributions by Siddhartha Chib ... [et al.] 205 $a2nd ed. 210 $aHoboken, N.J. $cWiley-Interscience$dc2003 215 $a1 online resource (591 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-471-34843-0 320 $aIncludes bibliographical references and index. 327 $aSubjective 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 327 $a2.2.3 Logical Probability2.2.4 Kolmogorov Axiom System of Frequency Probability; 2.2.5 Savage System of Axioms of Subjective Probability; 2.2.6 Re?nyi 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 327 $a3. 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 327 $a4.3 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 327 $aExample 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 327 $a5. Prior Distributions 330 $aShorter, more concise chapters provide flexible coverage of the subject.Expanded coverage includes: uncertainty and randomness, prior distributions, predictivism, estimation, analysis of variance, and classification and imaging.Includes topics not covered in other books, such as the de Finetti Transform.Author S. James Press is the modern guru of Bayesian statistics. 410 0$aWiley series in probability and statistics. 606 $aBayesian statistical decision theory 615 0$aBayesian statistical decision theory. 676 $a519.542 700 $aPress$b S. James$0280166 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830030103321 996 $aSubjective and objective Bayesian statistics$94124819 997 $aUNINA