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Bayesian data analysis / Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin.
Author
Gelman, Andrew
[Browse]
Format
Book
Language
English
Εdition
Third edition.
Published/Created
Boca Raton : CRC Press, [2013]
©2013
Description
xiv, 667 pages : illustrations ; 27 cm
Availability
Copies in the Library
Location
Call Number
Status
Location Service
Notes
Lewis Library - Stacks
QA279.5 .G45 2013
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Details
Subject(s)
Bayesian statistical decision theory
[Browse]
Mathematical statistics
[Browse]
Author
Carlin, John B.
[Browse]
Stern, Hal Steven
[Browse]
Dunson, David B.
[Browse]
Vehtari, Aki
[Browse]
Rubin, Donald B.
[Browse]
Series
Texts in statistical science
[More in this series]
Chapman & Hall/CRC texts in statistical science
Summary note
"Preface This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference starting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian methods in applied statistics for general users of and researchers in applied statistics. Although introductory in its early sections, the book is definitely not elementary in the sense of a first text in statistics. The mathematics used in our book is basic probability and statistics, elementary calculus, and linear algebra. A review of probability notation is given in Chapter 1 along with a more detailed list of topics assumed to have been studied. The practical orientation of the book means that the reader's previous experience in probability, statistics, and linear algebra should ideally have included strong computational components. To write an introductory text alone would leave many readers with only a taste of the conceptual elements but no guidance for venturing into genuine practical applications, beyond those where Bayesian methods agree essentially with standard non-Bayesian analyses. On the other hand, we feel it would be a mistake to present the advanced methods without first introducing the basic concepts from our data-analytic perspective. Furthermore, due to the nature of applied statistics, a text on current Bayesian methodology would be incomplete without a variety of worked examples drawn from real applications. To avoid cluttering the main narrative, there are bibliographic notes at the end of each chapter and references at the end of the book"-- Provided by publisher.
Notes
"Version date: 20150505"--T.p. verso.
"A Chapman and Hall Book."
Bibliographic references
Includes bibliographical references (pages 609-642) and indexes.
Contents
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.
Show 23 more Contents items
Other title(s)
BDA3
ISBN
9781439840955
1439840954
LCCN
2013039507
OCLC
950574272
Statement on language in description
Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage.
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Bayesian data analysis / Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin.
id
9976473803506421