The Resource Bayesian data analysis, Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
Bayesian data analysis, Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
Resource Information
The item Bayesian data analysis, Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 2 library branches.
Resource Information
The item Bayesian data analysis, Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 2 library branches.
 Summary
 "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 nonBayesian 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 dataanalytic 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"
 Language
 eng
 Edition
 Third edition.
 Extent
 1 online resource (xiv, 661 pages)
 Contents

 Part I: Fundamentals of Bayesian inference. Probability and inference
 Singleparameter models
 Introduction to multiparameter models
 Asymptotics and connections to nonBayesian 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
 Isbn
 9781439898208
 Label
 Bayesian data analysis
 Title
 Bayesian data analysis
 Statement of responsibility
 Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
 Title variation
 BDA3
 Language
 eng
 Summary
 "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 nonBayesian 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 dataanalytic 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"
 Assigning source
 Provided by publisher
 Cataloging source
 YDXCP
 http://library.link/vocab/creatorName
 Gelman, Andrew
 Dewey number
 519.5/42
 Illustrations
 illustrations
 Index
 index present
 LC call number
 QA279.5
 LC item number
 .G45 2014
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 NLM call number
 QA 279.5
 http://library.link/vocab/relatedWorkOrContributorName

 Carlin, John B.
 Stern, Hal Steven
 Dunson, David B.
 Vehtari, Aki
 Rubin, Donald B.
 Series statement
 Chapman & Hall/CRC texts in statistical science
 http://library.link/vocab/subjectName

 Bayesian statistical decision theory
 MATHEMATICS
 MATHEMATICS
 Bayesian statistical decision theory
 Label
 Bayesian data analysis, Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
 Bibliography note
 Includes bibliographical references (pages 607639) and indexes
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents
 Part I: Fundamentals of Bayesian inference. Probability and inference  Singleparameter models  Introduction to multiparameter models  Asymptotics and connections to nonBayesian 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
 Control code
 909477393
 Dimensions
 unknown
 Edition
 Third edition.
 Extent
 1 online resource (xiv, 661 pages)
 Form of item
 online
 Isbn
 9781439898208
 Lccn
 2013039507
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other control number

 40023006895
 7448428
 Other physical details
 illustrations.
 http://library.link/vocab/ext/overdrive/overdriveId
 1438153
 Specific material designation
 remote
 System control number
 (OCoLC)909477393
 Label
 Bayesian data analysis, Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
 Bibliography note
 Includes bibliographical references (pages 607639) and indexes
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents
 Part I: Fundamentals of Bayesian inference. Probability and inference  Singleparameter models  Introduction to multiparameter models  Asymptotics and connections to nonBayesian 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
 Control code
 909477393
 Dimensions
 unknown
 Edition
 Third edition.
 Extent
 1 online resource (xiv, 661 pages)
 Form of item
 online
 Isbn
 9781439898208
 Lccn
 2013039507
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other control number

 40023006895
 7448428
 Other physical details
 illustrations.
 http://library.link/vocab/ext/overdrive/overdriveId
 1438153
 Specific material designation
 remote
 System control number
 (OCoLC)909477393
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<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.library.missouri.edu/portal/BayesiandataanalysisAndrewGelmanJohnB./DFyL9jEdx2Q/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/portal/BayesiandataanalysisAndrewGelmanJohnB./DFyL9jEdx2Q/">Bayesian data analysis, Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>