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 WinBUGS – the most-widely used Bayesian analysis software in the world – and R – an open-source statistical software. The book is supported by a Website featuringWinBUGS and R code, data sets, and solutions to exercises. Ezekiel Chinyio, School of Property, Construction & Planning, University of Central England, Birmingham Professor Akintola Akintoye, School of the Builtand Natural Environment, Glasgow Caledonian University Professor Peter Barrett, School of the Built Environment, University of Salford INDICE: List of FiguresList of TablesPrefaceAcknowledgmentsIntroductionPart I Introducing Bayesian Analysis1 The foundations of Bayesian inference1.1 What is probability?1.2 Subjective probability in Bayesian statistics1.3 Bayes theorem, discrete case1.4 Bayes theorem, continuous parameter1.5 Parameters as random variables, beliefs as distributions1.6 Communicating the results of a Bayesian analysis1.7 Asymptotic properties of posterior distributions1.8 Bayesian hypothesis testing1.9 From subjective beliefs to parameters and models1.10 Historical note2 Getting started: Bayesian analysis for simple models2.1 Learning about probabilities, rates and proportions2.2 Associations between binary variables2.3 Learning from counts2.4 Learning about a normal mean and variance2.5 Regression models2.6 Further readingPart II Simulation Based Bayesian Analysis3 Monte Carlo methods3.1 Simulation consistency3.2 Inference for functions of parameters3.3 Marginalization via Monte Carlo integration3.4 Sampling algorithms3.5 Further reading4 Markov chains4.1 Notation and definitions4.2 Properties of Markov chains4.3 Convergence of Markov chains4.4 Limit theorems for Markov chains4.5 Further reading5 Markov chain Monte Carlo5.1 Metropolis-Hastings algorithm5.2 Gibbs sampling6 Implementing Markov chain Monte Carlo6.1 Software for Markov chain Monte Carlo6.2 Assessing convergence and run-length6.3 Working with BUGS/JAGS from R6.4 Tricks of the trade6.5 Other examples6.6 Further readingPart III Advanced Applications in the Social Sciences7 Hierarchical Statistical Models7.1 Data and parameters that vary by groups: the case for hierarchical modeling7.2 ANOVA as a hierarchical model7.3 Hierarchical models for longitudinal data7.4 Hierarchical models for non-normal data7.5 Multi-level models8 Bayesian analysis of choice making8.1 Regression models for binary responses8.2 Ordered outcomes8.3 Multinomial outcomes8.4 Multinomial probit9 Bayesian approaches to measurement9.1 Bayesian inference for latent states9.2 Factor analysis9.3 Item-response models9.4 Dynamic measurement modelsPart IV AppendicesAppendix A: Working with vectors and matricesAppendix B: Probability reviewB.1 Foundations of probabilityB.2 Probability densities and mass functionsB.3 Convergence of sequences of random variabalesAppendix C: Proofs of selected propositionsC.1 Products of normal densitiesC.2 Conjugate analysis of normal dataC.3 Asymptotic normality of the posterior densityTopic indexAuthor index
- ISBN: 978-0-470-01154-6
- Editorial: John Wiley & Sons
- Encuadernacion: Desconocida
- Fecha Publicación: 01/12/2008
- Nº Volúmenes: 1
- Idioma: Inglés