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Nonparametric Bayesian Inference in Biostatistics

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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.

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Detail Information

Series Title

Frontiers in Probability and the Statistical Sciences

Call Number

-

Publisher

Cham, Switzerland : Springer., 2015

Collation

XVII, 448

Language

English

ISBN/ISSN

978-3-319-19517-9

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

1

Subject(s)

Statistical Theory and Methods
Biostatistics

Specific Detail Info

-

Statement of Responsibility

Riten Mitra

Other Information

Cataloger

sara

Source

-

Validator

Taufik

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