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Description: |
xxv, 668 pages : illustrations, maps ; 25 cm |
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Bibliography Note: |
Includes bibliographical references (pages 611-646) and indexes. |
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Contents Note: |
Contents: Part I: Fundamentals of Bayesian inference -- Background -- Single-parameter models -- Introduction to multiparameter models -- Large-sample inference and frequency properties of Bayesian inference -- Part II: Fundamentals of Bayesian data analysis -- Hierarchical models -- Model checking and improvement -- Modeling accounting for data collection -- Connections and challenges -- General advice -- Part III: Advanced computation -- Overview of computation -- Posterior simulation -- Approximations based on posterior modes -- Special topics in computation -- Part IV: Regression models -- Introduction to regression models -- Hierarchical linear models -- Generalized linear models -- Models for robust inference -- Part V: Specific models and problems -- Mixture models -- Multivariate models -- Nonlinear models -- Models for missing data -- Decision analysis -- Appendixes. Standard probability distributions -- Outline of proofs of asymptotic theorems -- Example of computation in R and Bugs. |
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Summary, Etc. Note: |
Summary: The second edition of Bayesian Data Analysis continues to emphasize practice over theory, clearly describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide detailed guidance on all aspects of Bayesian data analysis and include many examples of real statistical analyses, based on their own research. |
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Table of contents application/pdf http://www.ulb.tu-darmstadt.de/tocs/113302258.pdf 20090711000000 |
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Series Added Entry: |
Texts in statistical science. |