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    Bayesian Data Analysis

    By: Andrew Gelman, Donald B. Rubin

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    Emphasizing practice over theory, Bayesian Data Analysis describes how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Using examples from the authors' own experiences, the book focuses on modern computational tools and obtains inferences using computer simulations. The author studies a variety of models and a range of issues, from incorporating survey design information to checking model adequacy, all from a consistent Bayesian viewpoint. The book's unique features include thorough discussions of the methods for checking Bayesian models and the role of the design of data collection in influencing Bayesian statistical analysis.

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