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Book Detail
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Bayesian Statistics for the Social Sciences
Kaplan, David
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Category: Nursing: Research Publisher: Guilford Press Imprint: The Guilford Press Affiliation: University of Wisconsin-Madison Audience: Professional and scholarly Dimensions: 9.25 x 1.0 x 6.13 in
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Synopsis:
Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and practical skills they need to apply Bayesian methodologies to their data analysis problems. Part I addresses the elements of Bayesian inference, including exchangeability, likelihood, prior/posterior distributions, and the Bayesian central limit theorem. Part II covers Bayesian hypothesis testing, model building, and linear regression analysis, carefully explaining the differences between the Bayesian and frequentist approaches. Part III extends Bayesian statistics to multilevel modeling and modeling for continuous and categorical latent variables. Kaplan closes with a discussion of philosophical issues and argues for an "evidence- based" framework for the practice of Bayesian statistics.
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