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Applied Bayesian Statistics / Mary Kathryn Cowles / Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer (2013)
Titre : Applied Bayesian Statistics : With R and OpenBUGS Examples Type de document : document électronique Auteurs : Mary Kathryn Cowles ; SpringerLink (Online service) Editeur : Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer Année de publication : 2013 Collection : Springer Texts in Statistics, ISSN 1431-875X num. 98 Importance : XIV, 232 p. 68 illus., 27 illus. in color Présentation : online resource ISBN/ISSN/EAN : 978-1-4614-5696-4 Langues : Anglais (eng) Descripteurs : Biostatistique La biostatistique (appelée aussi biométrie) est l'application des statistiques à un large éventail de sujets en biologie. Cela englobe la conception des expériences biologiques (dans la médecine et l'agriculture), la collecte des informations, la compilation et analyse des données chiffrées de ces expériences, l'interprétation des résultats en vue d'avancer une conclusion., Logiciel statistique R , Marketing , Modèle de régressionTags : Statistics Mathematical statistics Statistical Theory and Methods Résumé : This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs in Statistics, Biostatistics, Engineering, Economics, Marketing, Pharmacy, and Psychology. The goal of the book is to impart the basics of designing and carrying out Bayesian analyses, and interpreting and communicating the results. In addition, readers will learn to use the predominant software for Bayesian model-fitting, R and OpenBUGS. The practical approach this book takes will help students of all levels to build understanding of the concepts and procedures required to answer real questions by performing Bayesian analysis of real data. Topics covered include comparing and contrasting Bayesian and classical methods, specifying hierarchical models, and assessing Markov chain Monte Carlo output. Mary Kathryn (Kate) Cowles taught Suzuki piano for many years before going to graduate school in Biostatistics. Her research areas are Bayesian and computational statistics, with application to environmental science. She is on the faculty of Statistics at The University of Iowa Note de contenu : What is Bayesian statistics? -- Review of probability -- Introduction to one-parameter models -- Inference for a population proportion -- Special considerations in Bayesian inference -- Other one-parameter models and their conjugate priors -- More realism please: Introduction to multiparameter models -- Fitting more complex Bayesian models: Markov chain Monte Carlo -- Hierarchical models, and more on convergence assessment -- Regression and hierarchical regression models -- Model Comparison, Model Checking, and Hypothesis Testing -- References -- Index Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=108092 Applied Bayesian Statistics : With R and OpenBUGS Examples [document électronique] / Mary Kathryn Cowles ; SpringerLink (Online service) . - Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer, 2013 . - XIV, 232 p. 68 illus., 27 illus. in color : online resource. - (Springer Texts in Statistics, ISSN 1431-875X; 98) .
ISBN : 978-1-4614-5696-4
Langues : Anglais (eng)
Descripteurs : Biostatistique La biostatistique (appelée aussi biométrie) est l'application des statistiques à un large éventail de sujets en biologie. Cela englobe la conception des expériences biologiques (dans la médecine et l'agriculture), la collecte des informations, la compilation et analyse des données chiffrées de ces expériences, l'interprétation des résultats en vue d'avancer une conclusion., Logiciel statistique R , Marketing , Modèle de régressionTags : Statistics Mathematical statistics Statistical Theory and Methods Résumé : This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs in Statistics, Biostatistics, Engineering, Economics, Marketing, Pharmacy, and Psychology. The goal of the book is to impart the basics of designing and carrying out Bayesian analyses, and interpreting and communicating the results. In addition, readers will learn to use the predominant software for Bayesian model-fitting, R and OpenBUGS. The practical approach this book takes will help students of all levels to build understanding of the concepts and procedures required to answer real questions by performing Bayesian analysis of real data. Topics covered include comparing and contrasting Bayesian and classical methods, specifying hierarchical models, and assessing Markov chain Monte Carlo output. Mary Kathryn (Kate) Cowles taught Suzuki piano for many years before going to graduate school in Biostatistics. Her research areas are Bayesian and computational statistics, with application to environmental science. She is on the faculty of Statistics at The University of Iowa Note de contenu : What is Bayesian statistics? -- Review of probability -- Introduction to one-parameter models -- Inference for a population proportion -- Special considerations in Bayesian inference -- Other one-parameter models and their conjugate priors -- More realism please: Introduction to multiparameter models -- Fitting more complex Bayesian models: Markov chain Monte Carlo -- Hierarchical models, and more on convergence assessment -- Regression and hierarchical regression models -- Model Comparison, Model Checking, and Hypothesis Testing -- References -- Index Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=108092