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Nonparametric Bayesian Inference in Biostatistics

  • Book
  • © 2015

Overview

Editors:
  1. Riten Mitra

    1. Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Peter Müller

    1. Department of Mathematics, University of Texas, Austin, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • First comprehensive review of a fast growing field
  • Accessible to readers with a working graduate level knowledge of statistics and interest in Bayesian inference and biomedical applications
  • Most chapters include substantial applications that illustrate methods and models by addressing real research questions
  • Proceeds of this book go to the International Society for Bayesian Analysis/Section on Bayesian Nonparametrics (ISBA/BNP)
  • Chapters cover applications in clinical trials, spatial inference, proteomics, genomics, clustering, survival analysis and ROC curves

Part of the book series: Frontiers in Probability and the Statistical Sciences (FROPROSTAS)

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Table of contents (21 chapters)

  1. Front Matter

    Pages i-xvii
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  2. Introduction

    1. Front Matter

      Pages 1-1
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    2. Bayesian Nonparametric Models

      • Peter Müller, Riten Mitra
      Pages 3-13

    3. Bayesian Nonparametric Biostatistics

      • Wesley O. Johnson, Miguel de Carvalho
      Pages 15-54

  3. Genomics and Proteomics

    1. Front Matter

      Pages 55-55
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    2. Bayesian Shape Clustering

      • Zhengwu Zhang, Debdeep Pati, Anuj Srivastava
      Pages 57-75

    3. Estimating Latent Cell Subpopulations with Bayesian Feature Allocation Models

      • Yuan Ji, Subhajit Sengupta, Juhee Lee, Peter Müller, Kamalakar Gulukota
      Pages 77-95

    4. Species Sampling Priors for Modeling Dependence: An Application to the Detection of Chromosomal Aberrations

      • Federico Bassetti, Fabrizio Leisen, Edoardo Airoldi, Michele Guindani
      Pages 97-114

    5. Modeling the Association Between Clusters of SNPs and Disease Responses

      • Raffaele Argiento, Alessandra Guglielmi, Chuhsing Kate Hsiao, Fabrizio Ruggeri, Charlotte Wang
      Pages 115-134

    6. Bayesian Inference on Population Structure: From Parametric to Nonparametric Modeling

      • Maria De Iorio, Stefano Favaro, Yee Whye Teh
      Pages 135-151

    7. Bayesian Approaches for Large Biological Networks

      • Yang Ni, Giovanni M. Marchetti, Veerabhadran Baladandayuthapani, Francesco C. Stingo
      Pages 153-173

    8. Nonparametric Variable Selection, Clustering and Prediction for Large Biological Datasets

      • Subharup Guha, Sayantan Banerjee, Chiyu Gu, Veerabhadran Baladandayuthapani
      Pages 175-192

  4. Survival Analysis

    1. Front Matter

      Pages 193-193
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    2. Markov Processes in Survival Analysis

      • Luis E. Nieto-Barajas
      Pages 195-213

    3. Bayesian Spatial Survival Models

      • Haiming Zhou, Timothy Hanson
      Pages 215-246

    4. Fully Nonparametric Regression Modelling of Misclassified Censored Time-to-Event Data

      • Alejandro Jara, María José García-Zattera, Arnošt Komárek
      Pages 247-267

  5. Random Functions and Response Surfaces

    1. Front Matter

      Pages 269-269
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    2. Neuronal Spike Train Analysis Using Gaussian Process Models

      • Babak Shahbaba, Sam Behseta, Alexander Vandenberg-Rodes
      Pages 271-285

    3. Bayesian Analysis of Curves Shape Variation Through Registration and Regression

      • Donatello Telesca
      Pages 287-310

    4. Biomarker-Driven Adaptive Design

      • Yanxun Xu, Yuan Ji, Peter Müller
      Pages 311-326
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Keywords

About this book

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.

 

Editors and Affiliations

  • Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, USA

    Riten Mitra

  • Department of Mathematics, University of Texas, Austin, USA

    Peter Müller

About the editors

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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