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• catalog abstract ""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."--BOOK JACKET.".
• catalog contributor b8971462.
• catalog created "1996, 1997, [c1995].".
• catalog date "1995".
• catalog date "1996, 1997, [c1995].".
• catalog dateCopyrighted "1996, 1997, [c1995].".
• catalog description ""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."--BOOK JACKET.".
• catalog description "Includes bibliographical references (p. [489]-512) and indexes.".
• catalog description "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.".
• catalog extent "xix, 526 p. :".
• catalog identifier "0412039915".
• catalog isPartOf "Chapman & Hall texts in statistical science series".
• catalog isPartOf "Texts in statistical science.".
• catalog issued "1995".
• catalog issued "1996, 1997, [c1995].".
• catalog language "eng".
• catalog publisher "London ; New York : Chapman & Hall/CRC,".
• catalog subject "519.542 20".
• catalog subject "Bayesian methods".
• catalog subject "Bayesian statistical decision theory.".
• catalog subject "Mathematical statistics.".
• catalog subject "QA279.5 .B396 1995".
• catalog tableOfContents "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.".
• catalog title "Bayesian data analysis / Andrew Gelman ... [et al.]".
• catalog type "text".