Nonparametric Bayesian Inference in Biostatistics, 1st ed. 2015 Frontiers in Probability and the Statistical Sciences Series

Part I Introduction.- Bayesian Nonparametric Models.- Bayesian Nonparametric Biostatistics.- Part II Genomics and Proteomics.- Bayesian Shape Clustering.- Estimating Latent Cell Subpopulations with Bayesian Feature Allocation Models.- Species Sampling Priors for Modeling Dependence: An Application to the Detection of Chromosomal Aberrations.- Modeling the Association Between Clusters of SNPs and Disease Responses.- Bayesian Inference on Population Structure: from Parametric to Nonparametric Modeling.- Bayesian Approaches for Large Biological Networks.- Nonparametric Variable Selection, Clustering and Prediction for Large Biological Datasets.- Part III Survival Analysis.- Markov Processes in Survival Analysis.- Bayesian Spatial Survival Models.- Fully Nonparametric Regression Modelling of Misclassified Censored Time-to-Event Data.- Part IV Random Functions and Response Surfaces.- Neuronal Spike Train Analysis Using Gaussian Process Models.- Bayesian Analysis of Curves Shape Variation through Registration and Regression.- Biomarker-Driven Adaptive Design.- Bayesian Nonparametric Approaches for ROC Curve Inference.- Part V Spatial Data.- Spatial Bayesian Nonparametric Methods.- Spatial Species Sampling and Product Partition Models.- Spatial Boundary Detection for Areal Counts.- A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs.- Bayesian Nonparametrics for Missing Data in Longitudinal Clinical Trials.

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.

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