ENGLISH

Nonparametric Bayesian Inference in Biostatistics

By Peter Müller (eds.) , Riten Mitra

Book information

Publisher
Springer International Publishing
Year
2015
ISBN
978-3-319-19517-9, 978-3-319-19518-6
DOI
10.1007/978-3-319-19518-6
Language
english
Format
PDF
Filesize
10 MB (10476197 bytes)
Series
Frontiers in Probability and the Statistical Sciences
Edition
1
Pages
XVII, 448\448
Time added
2015-11-25 16:51:03
Health Sciences; Biostatistics; Statistical TheoryMedicineStatistics for Life Sciences

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.

Similar books