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Bayesian nonparametric data analysis

This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data a...

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Detalles Bibliográficos
Autores principales: Müller, Peter, Quintana, Fernando Andres, Jara, Alejandro, Hanson, Tim
Lenguaje:eng
Publicado: Springer 2015
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-18968-0
http://cds.cern.ch/record/2032373
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author Müller, Peter
Quintana, Fernando Andres
Jara, Alejandro
Hanson, Tim
author_facet Müller, Peter
Quintana, Fernando Andres
Jara, Alejandro
Hanson, Tim
author_sort Müller, Peter
collection CERN
description This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.
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institution Organización Europea para la Investigación Nuclear
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publishDate 2015
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spelling cern-20323732021-04-21T20:09:56Zdoi:10.1007/978-3-319-18968-0http://cds.cern.ch/record/2032373engMüller, PeterQuintana, Fernando AndresJara, AlejandroHanson, TimBayesian nonparametric data analysisMathematical Physics and MathematicsThis book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.Springeroai:cds.cern.ch:20323732015
spellingShingle Mathematical Physics and Mathematics
Müller, Peter
Quintana, Fernando Andres
Jara, Alejandro
Hanson, Tim
Bayesian nonparametric data analysis
title Bayesian nonparametric data analysis
title_full Bayesian nonparametric data analysis
title_fullStr Bayesian nonparametric data analysis
title_full_unstemmed Bayesian nonparametric data analysis
title_short Bayesian nonparametric data analysis
title_sort bayesian nonparametric data analysis
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-319-18968-0
http://cds.cern.ch/record/2032373
work_keys_str_mv AT mullerpeter bayesiannonparametricdataanalysis
AT quintanafernandoandres bayesiannonparametricdataanalysis
AT jaraalejandro bayesiannonparametricdataanalysis
AT hansontim bayesiannonparametricdataanalysis