Bayesian analysis for the social sciences / Simon Jackman.

Includes bibliographical references (pages 535-552) and indexes.

Bayesian 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; 2 Getting started: Bayesian analysis for simple models; Part II Simulation Based Bayesian Analysis; 3 Monte Carlo methods; 4 Markov chains; 5 Markov chain Monte Carlo; 6 Implementing Markov chain Monte Carlo; Part III Advanced Applications in the Social Sciences; 7 Hierarchical Statistical Models; 8 Bayesian analysis of choice making; 9 Bayesian approaches to measurement; Part IV Appendices.

Bayesian 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.

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