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Bayesian Essentials with R
Buch von Christian P. Robert (u. a.)
Sprache: Englisch

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Beschreibung
This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications.
Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable.

Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels. It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics.
This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications.
Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable.

Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels. It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics.
Über den Autor

Jean-Michel Marin is Professor of Statistics at Université Montpellier 2, France, and Head of the Mathematics and Modelling research unit. He has written over 40 papers on Bayesian methodology and computing, as well as worked closely with population geneticists over the past ten years.

Christian Robert is Professor of Statistics at Université Paris-Dauphine, France. He has written over 150 papers on Bayesian Statistics and computational methods and is the author or co-author of seven books on those topics, including The Bayesian Choice (Springer, 2001), winner of the ISBA DeGroot Prize in 2004. He is a Fellow of the Institute of Mathematical Statistics, the Royal Statistical Society and the American Statistical Society. He has been co-editor of the Journal of the Royal Statistical Society, Series B, and in the editorial boards of the Journal of the American Statistical Society, the Annals of Statistics, Statistical Science, and Bayesian Analysis. He is also a recipient of an Erskine Fellowship from the University of Canterbury (NZ) in 2006 and a senior member of the Institut Universitaire de France (2010-2015).

Zusammenfassung

New Complete Solutions Manual for readers available on Springer book page

No prior knowledge of R required to learn the essentials for using it with Bayesian statistics

Each chapter includes exercises that are both methodology and data-based

Important textbook for students, practitioners, and applied statisticians

Includes supplementary material: [...]

Inhaltsverzeichnis
User's Manual.- Normal Models.- Regression and Variable Selection.- Generalized Linear Models.- Capture-Recapture Experiments.- Mixture Models.- Time Series.- Image Analysis.- References.- Index.
Details
Erscheinungsjahr: 2013
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Reihe: Springer Texts in Statistics
Inhalt: xiv
296 S.
37 s/w Illustr.
38 farbige Illustr.
296 p. 75 illus.
38 illus. in color.
ISBN-13: 9781461486862
ISBN-10: 1461486866
Sprache: Englisch
Herstellernummer: 80017424
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Robert, Christian P.
Marin, Jean-Michel
Auflage: 2nd ed. 2014
Hersteller: Springer US
Springer New York
Springer US, New York, N.Y.
Springer Texts in Statistics
Maße: 241 x 160 x 23 mm
Von/Mit: Christian P. Robert (u. a.)
Erscheinungsdatum: 29.10.2013
Gewicht: 0,635 kg
Artikel-ID: 105688487
Über den Autor

Jean-Michel Marin is Professor of Statistics at Université Montpellier 2, France, and Head of the Mathematics and Modelling research unit. He has written over 40 papers on Bayesian methodology and computing, as well as worked closely with population geneticists over the past ten years.

Christian Robert is Professor of Statistics at Université Paris-Dauphine, France. He has written over 150 papers on Bayesian Statistics and computational methods and is the author or co-author of seven books on those topics, including The Bayesian Choice (Springer, 2001), winner of the ISBA DeGroot Prize in 2004. He is a Fellow of the Institute of Mathematical Statistics, the Royal Statistical Society and the American Statistical Society. He has been co-editor of the Journal of the Royal Statistical Society, Series B, and in the editorial boards of the Journal of the American Statistical Society, the Annals of Statistics, Statistical Science, and Bayesian Analysis. He is also a recipient of an Erskine Fellowship from the University of Canterbury (NZ) in 2006 and a senior member of the Institut Universitaire de France (2010-2015).

Zusammenfassung

New Complete Solutions Manual for readers available on Springer book page

No prior knowledge of R required to learn the essentials for using it with Bayesian statistics

Each chapter includes exercises that are both methodology and data-based

Important textbook for students, practitioners, and applied statisticians

Includes supplementary material: [...]

Inhaltsverzeichnis
User's Manual.- Normal Models.- Regression and Variable Selection.- Generalized Linear Models.- Capture-Recapture Experiments.- Mixture Models.- Time Series.- Image Analysis.- References.- Index.
Details
Erscheinungsjahr: 2013
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Reihe: Springer Texts in Statistics
Inhalt: xiv
296 S.
37 s/w Illustr.
38 farbige Illustr.
296 p. 75 illus.
38 illus. in color.
ISBN-13: 9781461486862
ISBN-10: 1461486866
Sprache: Englisch
Herstellernummer: 80017424
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Robert, Christian P.
Marin, Jean-Michel
Auflage: 2nd ed. 2014
Hersteller: Springer US
Springer New York
Springer US, New York, N.Y.
Springer Texts in Statistics
Maße: 241 x 160 x 23 mm
Von/Mit: Christian P. Robert (u. a.)
Erscheinungsdatum: 29.10.2013
Gewicht: 0,635 kg
Artikel-ID: 105688487
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