LEADER 05391nam 2200637Ia 450 001 9910831183403321 005 20230207230645.0 010 $a1-282-35479-5 010 $a9786612354793 010 $a0-470-68662-6 010 $a0-470-68663-4 035 $a(CKB)1000000000822271 035 $a(EBL)470584 035 $a(OCoLC)502012717 035 $a(SSID)ssj0000289703 035 $a(PQKBManifestationID)11255012 035 $a(PQKBTitleCode)TC0000289703 035 $a(PQKBWorkID)10402315 035 $a(PQKB)11605697 035 $a(MiAaPQ)EBC470584 035 $a(EXLCZ)991000000000822271 100 $a20090917d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian analysis for the social sciences$b[electronic resource] /$fSimon Jackman 210 $aHoboken, NJ $cWiley$d2009 215 $a1 online resource (609 p.) 225 1 $aWiley series in probability and statistics. ;$vv.846 300 $aDescription based upon print version of record. 311 $a0-470-01154-8 320 $aIncludes bibliographical references and index. 327 $aBayesian Analysis for the Social Sciences; Contents; List of Figures; List of Tables; Preface; Acknowledgments; Introduction; Part I Introducing Bayesian Analysis; 1 The foundations of Bayesian inference; 1.1 What is probability?; 1.1.1 Probability in classical statistics; 1.1.2 Subjective probability; 1.2 Subjective probability in Bayesian statistics; 1.3 Bayes theorem, discrete case; 1.4 Bayes theorem, continuous parameter; 1.4.1 Conjugate priors; 1.4.2 Bayesian updating with irregular priors; 1.4.3 Cromwell's Rule; 1.4.4 Bayesian updating as information accumulation 327 $a1.5 Parameters as random variables, beliefs as distributions1.6 Communicating the results of a Bayesian analysis; 1.6.1 Bayesian point estimation; 1.6.2 Credible regions; 1.7 Asymptotic properties of posterior distributions; 1.8 Bayesian hypothesis testing; 1.8.1 Model choice; 1.8.2 Bayes factors; 1.9 From subjective beliefs to parameters and models; 1.9.1 Exchangeability; 1.9.2 Implications and extensions of de Finetti's Representation Theorem; 1.9.3 Finite exchangeability; 1.9.4 Exchangeability and prediction; 1.9.5 Conditional exchangeability and multiparameter models 327 $a1.9.6 Exchangeability of parameters: hierarchical modeling1.10 Historical note; 2 Getting started: Bayesian analysis for simple models; 2.1 Learning about probabilities, rates and proportions; 2.1.1 Conjugate priors for probabilities, rates and proportions; 2.1.2 Bayes estimates as weighted averages of priors and data; 2.1.3 Parameterizations and priors; 2.1.4 The variance of the posterior density; 2.2 Associations between binary variables; 2.3 Learning from counts; 2.3.1 Predictive inference with count data; 2.4 Learning about a normal mean and variance; 2.4.1 Variance known 327 $a2.4.2 Mean and variance unknown2.4.3 Conditionally conjugate prior; 2.4.4 An improper, reference prior; 2.4.5 Conflict between likelihood and prior; 2.4.6 Non-conjugate priors; 2.5 Regression models; 2.5.1 Bayesian regression analysis; 2.5.2 Likelihood function; 2.5.3 Conjugate prior; 2.5.4 Improper, reference prior; 2.6 Further reading; Part II Simulation Based Bayesian Analysis; 3 Monte Carlo methods; 3.1 Simulation consistency; 3.2 Inference for functions of parameters; 3.3 Marginalization via Monte Carlo integration; 3.4 Sampling algorithms; 3.4.1 Inverse-CDF method 327 $a3.4.2 Importance sampling3.4.3 Accept-reject sampling; 3.4.4 Adaptive rejection sampling; 3.5 Further reading; 4 Markov chains; 4.1 Notation and definitions; 4.1.1 State space; 4.1.2 Transition kernel; 4.2 Properties of Markov chains; 4.2.1 Existence of a stationary distribution, discrete case; 4.2.2 Existence of a stationary distribution, continuous case; 4.2.3 Irreducibility; 4.2.4 Recurrence; 4.2.5 Invariant measure; 4.2.6 Reversibility; 4.2.7 Aperiodicity; 4.3 Convergence of Markov chains; 4.3.1 Speed of convergence; 4.4 Limit theorems for Markov chains; 4.4.1 Simulation inefficiency 327 $a4.4.2 Central limit theorems for Markov chains 330 $aBayesian methods are increasingly being used in the social sciences, as the problems encountered lend themselves so naturally to the subjective qualities of Bayesian methodology. This book provides an accessible introduction to Bayesian methods, tailored specifically for social science students. It contains lots of real examples from political science, psychology, sociology, and economics, exercises in all chapters, and detailed descriptions of all the key concepts, without assuming any background in statistics beyond a first course. It features examples of how to implement the methods using W 410 0$aWiley series in probability and statistics. 606 $aSocial sciences$xStatistical methods 606 $aBayesian statistical decision theory 615 0$aSocial sciences$xStatistical methods. 615 0$aBayesian statistical decision theory. 676 $a519.5 676 $a519.542 700 $aJackman$b Simon$f1966-$01675139 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910831183403321 996 $aBayesian analysis for the social sciences$94040409 997 $aUNINA