The Resource Bayesian data analysis, Andrew Gelman [and others]

Bayesian data analysis, Andrew Gelman [and others]

Label
Bayesian data analysis
Title
Bayesian data analysis
Statement of responsibility
Andrew Gelman [and others]
Contributor
Subject
Language
eng
Summary
"Bayesian Data Analysis is a comprehensive treatment of the statistical analysis of data from a Bayesian perspective. Modern computational tools are emphasized, and inferences are typically obtained using computer simulations." "The principles of Bayesian analysis are described with an emphasis on practical rather than theoretical issues, and illustrated using actual data. A variety of models are considered, including linear regression, hierarchical (random effects) models, robust models, generalized linear models and mixture models." "Two important and unique features of this text are thorough discussions of the methods for checking Bayesian models and the role of the design of data collection in influencing Bayesian statistical analysis." "Issues of data collection, model formulation, computation, model checking and sensitivity analysis are all considered. The student or practising statistician will find that there is guidance on all aspects of Bayesian data analysis."--Jacket
Member of
Action
committed to retain for EAST
Cataloging source
EUN
Illustrations
illustrations
Index
index present
LC call number
QA279.5
LC item number
.B396 1995
Literary form
non fiction
Nature of contents
bibliography
http://library.link/vocab/relatedWorkOrContributorName
Gelman, Andrew
Series statement
Chapman & Hall texts in statistical science series
http://library.link/vocab/subjectName
  • Bayesian statistical decision theory
  • Mathematical statistics
  • inférence statistique
  • statistique appliquée
  • méthode Bayes
  • analyse bayesienne
  • Statistique bayésienne
  • Statistique mathématique
  • Bayesian statistical decision theory
  • Mathematical statistics
  • Methode van Bayes
  • Data-analyse
  • Bayes-Verfahren
  • Inférence
  • Statistique bayésienne
  • Analyse des données
  • Bayes-Verfahren
Label
Bayesian data analysis, Andrew Gelman [and others]
Instantiates
Publication
Bibliography note
Includes bibliographical references (p. [489]-512) and indexes
Carrier category
volume
Carrier category code
nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
List of models -- List of examples -- Preface -- pt. I. Fundamentals of Bayesian inference. 1. Background ; 2. Single-parameter models ; 3. Introduction to multiparameter models ; 4. Large-sample inference and connections to standard statistical methods -- pt. II. Fundamentals of Bayesian data analysis. 5. Hierarchical models ; 6. Model checking and sensitivity analysis ; 7. Study design in Bayesian analysis ; 8. Introduction to regression models -- pt. III. Advanced computation. 9. Approximations based on posterior modes ; 10. Posterior simulation and integration ; 11. Markov chain simulation -- pt. IV. Specific models. 12. Models for robust inference and sensitivity analysis ; 13. Hierarchical linear models ; 14. Generalized linear models ; 15. Multivariate models ; 16. Mixture models ; 17. Models for missing data ; 18. Concluding advice -- Appendixes. A. Standard probability distributions ; B. Outline of proofs of asymptotic theorems -- References -- Author index -- Subject index
Dimensions
25 cm.
Extent
xix, 526 pages
Isbn
9780412039911
Media category
unmediated
Media MARC source
rdamedia
Media type code
n
Other physical details
illustrations
System control number
  • (OCoLC)33233097
  • (OCoLC)ocm33233097
Label
Bayesian data analysis, Andrew Gelman [and others]
Publication
Bibliography note
Includes bibliographical references (p. [489]-512) and indexes
Carrier category
volume
Carrier category code
nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
List of models -- List of examples -- Preface -- pt. I. Fundamentals of Bayesian inference. 1. Background ; 2. Single-parameter models ; 3. Introduction to multiparameter models ; 4. Large-sample inference and connections to standard statistical methods -- pt. II. Fundamentals of Bayesian data analysis. 5. Hierarchical models ; 6. Model checking and sensitivity analysis ; 7. Study design in Bayesian analysis ; 8. Introduction to regression models -- pt. III. Advanced computation. 9. Approximations based on posterior modes ; 10. Posterior simulation and integration ; 11. Markov chain simulation -- pt. IV. Specific models. 12. Models for robust inference and sensitivity analysis ; 13. Hierarchical linear models ; 14. Generalized linear models ; 15. Multivariate models ; 16. Mixture models ; 17. Models for missing data ; 18. Concluding advice -- Appendixes. A. Standard probability distributions ; B. Outline of proofs of asymptotic theorems -- References -- Author index -- Subject index
Dimensions
25 cm.
Extent
xix, 526 pages
Isbn
9780412039911
Media category
unmediated
Media MARC source
rdamedia
Media type code
n
Other physical details
illustrations
System control number
  • (OCoLC)33233097
  • (OCoLC)ocm33233097

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