## Bayesian Methods: A Social and Behavioral Sciences Approach, Second EditionThe first edition of Bayesian Methods: A Social and Behavioral Sciences Approach helped pave the way for Bayesian approaches to become more prominent in social science methodology. While the focus remains on practical modeling and basic theory as well as on intuitive explanations and derivations without skipping steps, this second edition incorporates the latest methodology and recent changes in software offerings. New to the Second Edition Requiring only a basic working knowledge of linear algebra and calculus, this text is one of the few to offer a graduate-level introduction to Bayesian statistics for social scientists. It first introduces Bayesian statistics and inference, before moving on to assess model quality and fit. Subsequent chapters examine hierarchical models within a Bayesian context and explore MCMC techniques and other numerical methods. Concentrating on practical computing issues, the author includes specific details for Bayesian model building and testing and uses the R and BUGS software for examples and exercises. |

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This book isn't only for social scientists. It is an excellent and clear explaination of Bayesian Inference, useful for all scientists and engineers.

Instead of presenting the reader with pages of equations and a few sparse sentences of text, Gill presents a key equations, such as Bayes Rule and then devotes paragraphs of well written text to explain the meaning and signifcance of the equations.

The book also includes a lovely worked examples, including an example of a Bayes Markov chain.

I found this invaluable during my PhD in computer science.

Sebastien

### Other editions - View all

Bayesian Methods: A Social and Behavioral Sciences Approach, Second Edition Jeff Gill Limited preview - 2007 |

Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition Jeff Gill Limited preview - 2014 |

### Common terms and phrases

analysis assumptions asymptotic Bayes Factor Bayesian model Berger beta binomial BUGS calculate Casella chain values Chapter coeﬃcient computing conditional distributions conjugate prior convergence credible interval deﬁned deﬁnition diagnostic diﬀerent diﬃcult eﬀect empirical ergodic example explanatory variables exponential family exponential family form ﬁnd ﬁnding ﬁnite ﬁrst ﬁt ﬁxed ﬂexible frequentist Gelman Gibbs sampler given hierarchical model improper priors inﬁnite integral interest iterations Jeﬀreys likelihood function linear model marginal distribution marginal posterior Markov chain matrix MCMC Metropolis-Hastings algorithm model speciﬁcation non-Bayesian observed outcome variable posterior distribution posterior mean prior distribution prior speciﬁcations probability problem produce properties provides quantiles quantities random variable regression rejection sampling sample space setup signiﬁcance simulation speciﬁcation specify standard stationary distribution statistics step suﬃcient target distribution theoretical tion transition kernel uniform prior unknown parameter update variance vector zero