Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

By:

Scott M. Lynch

Format:

Hardback

Series:

Statistics for Social and Behavioral Sciences

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Description

"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.

Release date NZ

July 27th, 2007

Author

Scott M. Lynch

Audience

Professional & Vocational

Illustrations

XXVIII, 359 p.

Pages

359

Series

Statistics for Social and Behavioral Sciences

Dimensions

156x234x22

ISBN-13

9780387712642

Product ID

2435226

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