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|e rda
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|a QA279.5
|b .G45 2014
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|a MAT029000
|2 bisacsh
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| 100 |
1 |
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|a Gelman, Andrew,
|e author.
|
| 245 |
1 |
0 |
|a Bayesian data analysis /
|c Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin.
|
| 246 |
1 |
8 |
|a BDA3
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| 250 |
|
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|a Third edition.
|
| 264 |
|
1 |
|a Boca Raton :
|b CRC Press,
|c 2014.
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| 300 |
|
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|a xiv, 661 pages :
|b illustrations ;
|c 27 cm.
|
| 336 |
|
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|a text
|2 rdacontent
|
| 337 |
|
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|a unmediated
|2 rdamedia
|
| 338 |
|
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|a volume
|2 rdacarrier
|
| 490 |
1 |
|
|a Chapman & Hall/CRC texts in statistical science
|
| 504 |
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|a Includes bibliographical references (pages 607-639) and indexes.
|
| 505 |
0 |
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|a Part I: Fundamentals of Bayesian inference. Probability and inference -- Single-parameter models -- Introduction to multiparameter models -- Asymptotics and connections to non-Bayesian approaches -- Hierarchical models -- Part II: Fundamentals of Bayesian data analysis. Model checking -- Evaluating, comparing, and expanding models -- Modeling accounting for data collection -- Decision analysis -- Part III: Advanced computation. Introduction to Bayesian computation -- Basics of Markov chain simulation -- Computationally efficient Markov chain simulation -- Modal and distributional approximations -- Part IV: Regression models. Introduction to regression models -- Hierarchical linear models -- Generalized linear models -- Models for robust inference -- Models for missing data -- Part V: Nonlinear and nonparametric models. Parametric nonlinear models -- Basis function models -- Gaussian process models -- Finite mixture models -- Dirichlet process models -- A. Standard probability distributions -- B. Outline of proofs of limit theorems -- Computation in R and Stan.
|
| 650 |
|
0 |
|a Bayesian statistical decision theory.
|
| 830 |
|
0 |
|a Texts in statistical science.
|
| 907 |
|
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|a .b19453358
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