Normal view
MARC view
Bayesian data analysis / Andrew Gelman ... [et al.].
Series: Texts in statistical sciencePublication details: Boca Raton, Fla. : Chapman & Hall/CRC, c2004.Edition: 2nd edDescription: xxv, 668 p. : ill., maps ; 25 cmContent type:- text
- unmediated
- volume
- 9781584883883 :
- 158488388X :
- QA279.5 .B386 2004
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.
| Item type | Current library | Home library | Shelving location | Call number | Status | Barcode | |
|---|---|---|---|---|---|---|---|
Books
|
American University in Dubai | American University in Dubai | Main Collection | QA 279.5 .B386 2004 (Browse shelf(Opens below)) | Available | 5143891 |
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.
Includes bibliographical references (p. 611-646) and indexes.
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