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.
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
359
Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
359Paperback(Softcover reprint of hardcover 1st ed. 2007)
Product Details
ISBN-13: | 9781441924346 |
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Publisher: | Springer New York |
Publication date: | 11/19/2010 |
Series: | Statistics for Social and Behavioral Sciences |
Edition description: | Softcover reprint of hardcover 1st ed. 2007 |
Pages: | 359 |
Product dimensions: | 6.10(w) x 9.20(h) x 0.90(d) |