Introduction to Applied Bayesian Statistics and Estimation for Social Scientists (Statistics for Social and Behavioral Sciences)

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'Introduction to Applied Bayesian Statistics and Estimation for Social Scientists' covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.The first part of the book provides a detailed introduction to mathematical statistics and the Bayesian approach to statistics, as well as a thorough explanation of the rationale for using simulation methods to construct summaries of posterior distributions. Markov chain Monte Carlo (MCMC) methods - including the Gibbs sampler and the Metropolis-Hastings algorithm - are then introduced as general methods for simulating samples from distributions. Extensive discussion of programming MCMC algorithms, monitoring their performance, and improving them is provided before turning to the larger examples involving real social science models and data.

Introduction to Applied Bayesian Statistics and Estimation for Social Scientists (Statistics for Social and Behavioral Sciences) Review

If you are interested in this title, I believe I can safely assume the following:

1. You are social scientist involved in research, or someone with a difficult problem to solve, and believe statistics can help.
2. You do not find "classical" statistical methods convincing or useful.
3. You are unlikely to be a math or statistics major, with minimal expousure to advanced coursework in probablity theory, calculus. real analysis, or differential equations.

If that is the case, most existing books on Bayesian methods will literally look like they were written in Greek, with obscure notation that requires a dictionary on hand to decipher the meaning of certain symbols in an entirely different context.

This is not a condemnation of those texts--they are extremely important to a subset (pun intended) of people interested in the Bayesian approach to statistical analysis. But for the rest of us, getting anything useful out of them is tough going.

Not this book. If you can grasp set theory, and have some understanding of calculus and linear algebra, this book can get you started on finding ways to use Bayesian Analysis to solve problems. The author explains the competing philosophies of frequentist and Bayesian approaches succintly and clearly, pointing out where the results from the differing methods converge, as well as where they differ.

If your exposure to advanced math is a bit lacking, the author provides the useful procedures from Calculus and Linear Algebra in an appendix.

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