Bayesian Analysis for the Social Sciences
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 featuring WinBUGS and R code, and data sets.
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ANOVA model average Bayes estimates Bayes Rule Bayes Theorem Bayesian analysis Bayesian approach Bayesian inference binary binomial component compute conditional densities conditional distributions conjugate prior consider convergence covariates credible interval data augmentation data set Deﬁnition election Equation Example frequentist Gibbs sampler given hierarchical model hyperparameters ideal points implement independent indicators intercept interval inverse-Gamma density iterations JAGS latent variable likelihood function linear marginal posterior densities Markov chain mass function matrix maximum likelihood estimate MCMC algorithm MLEs Monte Carlo methods multivariate normal density Note observed panel of Figure panel shows parameter space polls posterior mean posterior predictive density posterior probability predictors prior density prior information probit model problem proposal density Proposition quantiles quantity regression analysis regression model relatively sampled values school-specific Section specific standard deviation statistical target density Theorem trace plots uniform prior variance vector vote shares zero