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Nonparametric Bayesian inference in biostatistics

As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research pro...

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Detalles Bibliográficos
Autores principales: Mitra, Riten, Müller, Peter
Lenguaje:eng
Publicado: Springer 2015
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-19518-6
http://cds.cern.ch/record/2040827
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author Mitra, Riten
Müller, Peter
author_facet Mitra, Riten
Müller, Peter
author_sort Mitra, Riten
collection CERN
description As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters cover: clinical trials, spatial inference, proteomics, genomics, clustering, survival analysis and ROC curve. Riten Mitra is Assistant Professor in the Department of Bioinformatics and Biostatistics at University of Louisville. His research interests include Bayesian graphical models and nonparametric Bayesian methods with a special emphasis on applications in genomics and bioinformatics. Peter Mueller is Professor in the Department of Mathematics and the Department of Statistics & Data Science at the University of Texas at Austin. He has published widely on nonparametric Bayesian statistics, with an emphasis on applications in biostatistics and bioinformatics.
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spelling cern-20408272021-04-21T20:08:19Zdoi:10.1007/978-3-319-19518-6http://cds.cern.ch/record/2040827engMitra, RitenMüller, PeterNonparametric Bayesian inference in biostatisticsMathematical Physics and MathematicsAs chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters cover: clinical trials, spatial inference, proteomics, genomics, clustering, survival analysis and ROC curve. Riten Mitra is Assistant Professor in the Department of Bioinformatics and Biostatistics at University of Louisville. His research interests include Bayesian graphical models and nonparametric Bayesian methods with a special emphasis on applications in genomics and bioinformatics. Peter Mueller is Professor in the Department of Mathematics and the Department of Statistics & Data Science at the University of Texas at Austin. He has published widely on nonparametric Bayesian statistics, with an emphasis on applications in biostatistics and bioinformatics.Springeroai:cds.cern.ch:20408272015
spellingShingle Mathematical Physics and Mathematics
Mitra, Riten
Müller, Peter
Nonparametric Bayesian inference in biostatistics
title Nonparametric Bayesian inference in biostatistics
title_full Nonparametric Bayesian inference in biostatistics
title_fullStr Nonparametric Bayesian inference in biostatistics
title_full_unstemmed Nonparametric Bayesian inference in biostatistics
title_short Nonparametric Bayesian inference in biostatistics
title_sort nonparametric bayesian inference in biostatistics
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-319-19518-6
http://cds.cern.ch/record/2040827
work_keys_str_mv AT mitrariten nonparametricbayesianinferenceinbiostatistics
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