Titlebook: Nonparametric Bayesian Inference in Biostatistics; Riten Mitra,Peter Müller Book 2015 Springer International Publishing Switzerland 2015 B

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Tempor

Bayesian Nonparametric Biostatisticsntribute to addressing some fundamental questions that arise in biomedical research. In particular, we review some modern Bayesian semi- and nonparametric approaches for modeling longitudinal, survival, and medical diagnostic outcome data. Our discussion includes methods for longitudinal data analys

Flat-Feet

Bayesian Shape Clusteringages. Existing model-based clustering techniques rely on simple probability models that are not generally valid for analyzing shapes of curves. In this chapter, we talk about an efficient Bayesian method to cluster curve data using a carefully chosen metric on the shape space. Rather than modeling t

dithiolethione

Estimating Latent Cell Subpopulations with Bayesian Feature Allocation Modelsd by mutations in sequence and structure at various genomic locations. Using next-generation sequencing data, we characterize tumor heterogeneity using Bayesian nonparametric inference. Specifically, we estimate the number of subclones in a tumor sample, and for each subclone, we estimate the subclo

STIT

Species Sampling Priors for Modeling Dependence: An Application to the Detection of Chromosomal Abernparametric priors can be characterized in terms of exchangeable species sampling sequences. However, in some applications, common exchangeability assumptions may not be appropriate. We discuss a generalization of species sampling sequences, where the weights in the predictive probability functions

PAC

Ordeal

Bayesian Inference on Population Structure: From Parametric to Nonparametric Modeling. The source populations are assumed to be in Hardy-Weinberg equilibrium, but the allelic frequencies of these populations and even the number of populations present in a sample are unknown. In this chapter we present a review of some Bayesian parametric and nonparametric models for making inference

禮節(jié)

Bayesian Approaches for Large Biological Networksression data. Methods that employ sparse priors have been particularly successful, as they are properly designed to analyze large datasets in which the amount of measured variables can be greater than the number of observations. Here, we describe Bayesian approaches for both undirected and directed

AVID

Nonparametric Variable Selection, Clustering and Prediction for Large Biological Datasets due to relatively small sample sizes and the presence of complex interaction patterns between a large number of covariates. We propose an efficient, nonparametric framework for simultaneous variable selection, clustering and prediction in high-throughput regression settings with continuous outcomes

monogamy