Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

Lynch, Scott M.

Hardcover

Series: Statistics for Social and Behavioral Sciences

General MathematicsGeneral SociologyProbability & Statistics

ISBN10: 038771264X
ISBN13: 9780387712642
Publisher: Springer
Published: Jul 27 2007
Pages: 359
Weight: 1.51
Height: 0.92 Width: 6.50 Depth: 9.38
Language: English

This book provides an introduction to the Bayesian approach to statistical analysis of data, written at a level that is accessible to a social science audience. The book covers the Bayesian approach from model development through the development and implementation of programs to estimate the model, through summation and interpretation of the output. The first part 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 Markov chain Monte Carlo algorithms, monitoring their performance, and improving them is provided before turning to the larger examples involving real social science models and data.

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