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Applied Bayesian statistics : with R and OpenBUGS examples / Mary Kathryn Cowles

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, Biostatist... Full description

PPN (Catalogue-ID): 728848988
Personen: Cowles, Mary Kathryn [VerfasserIn]
Format: Book Book
Language: English
Published: New York, Springer, [2013]
Series: Springer texts in statistics ([98])
RVK:

SK 830: Mathematik -- Monographien -- Wahrscheinlichkeitstheorie -- Statistische Entscheidungstheorie

ST 250: Informatik -- Monographien -- Software und -entwicklung -- Programmiersprachen -- Einzelne Programmiersprachen (A-Z)

SK 850: Mathematik -- Monographien -- Wahrscheinlichkeitstheorie -- Angewandte Statistik, Tabellen

QH 233: Wirtschaftswissenschaften -- Mathematik. Statistik. Ökonometrie. Unternehmensforschung -- Statistik -- Theoretische Statistik -- Häufigkeitsverteilungen. Stichprobenverteilungen. Schätztheorie. Testtheorie. Statistische Entscheidungstheorie

Subjects:

Bayes-Entscheidungstheorie

Physical Description: xiv, 232 Seiten, Diagramme.
Link: Inhaltsverzeichnis
Inhaltstext
Inhaltsverzeichnis
ISBN: 1-4614-5695-9
978-1-4614-5695-7

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520 |a 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. -- 
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