Téléchargez le livre : Nonparametric Bayesian Inference in Biostatistics

Nonparametric Bayesian Inference in Biostatistics



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Springer


Paru le : 2015-07-25



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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.
 
Pages
448 pages
Collection
n.c
Parution
2015-07-25
Marque
Springer
EAN papier
9783319195179
EAN PDF
9783319195186
Informations sur l'ebook
Nombre pages copiables
4
Nombre pages imprimables
44
Taille du fichier
10342 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
96,29 €
EAN EPUB
9783319195186
Informations sur l'ebook
Nombre pages copiables
4
Nombre pages imprimables
44
Taille du fichier
5878 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
96,29 €

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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