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Estimating mixture of dirichlet process models
Abstract Current Gibbs sampling schemes in mixture of Dirichlet process (MDP) models are restricted to using “conjugate” base measures that allow analytic evaluation of the transition probabilitiesExpand
An ANOVA Model for Dependent Random Measures
We consider dependent nonparametric models for related random probability distributions. For example, the random distributions might be indexed by a categorical covariate indicating the treatmentExpand
Bayesian curve fitting using multivariate normal mixtures
SUMMARY Problems of regression smoothing and curve fitting are addressed via predictive inference in a flexible class of mixture models. Multidimensional density estimation using Dirichlet mixtureExpand
Model-Based Clustering for Expression Data via a Dirichlet Process Mixture Model
This chapter describes a clustering procedure for microarray expression data based on a well-defined statistical model, specifically, a conjugate Dirichlet process mixture model. The clusteringExpand
Bayesian Adaptive Methods for Clinical Trials
Statistical Approaches for Clinical Trials Introduction Comparisons between Bayesian and frequentist approaches Adaptivity in clinical trials Features and use of the Bayesian adaptive approach BasicsExpand
A method for combining inference across related nonparametric Bayesian models
We consider the problem of combining inference in related nonparametric Bayes models. Analogous to parametric hierarchical models, the hierarchical extension formalizes borrowing strength across theExpand
DPpackage: Bayesian Semi- and Nonparametric Modeling in R
This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Expand
Nonparametric Bayesian data analysis
We review the current state of nonparametric Bayesian inference. The discussion follows a list of important statistical inference problems, including density estimation, regression, survivalExpand
FDR and Bayesian Multiple Comparisons Rules
We discuss Bayesian approaches to multiple comparison problems, using a decision theoretic perspective to critically compare competing approaches. Expand
Simulation Based Optimal Design
We review simulation based approaches to optimal design, with an emphasis on problems that cast optimal design as formal decision problems. Under this perspective we approach optimal design problemsExpand