Nonparametric Bayesian Inference in Biostatistics

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

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.

 

• Biostatistical inference
• Clinical sciences
• Genomics / proteomics
• Nonparametric Bayesian (BNP) approaches
• Survival analysis
• Survival regression

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

  Riten Mitra

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

  Peter Müller

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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