We introduce approaches to performing Bayesian nonparametric statistical inference for distribution functions exhibiting a stochastic ordering. We consider Polya tree prior distributions, and Bernstein polynomial prior distributions, and each prior provides an appealing and simple way of introducing the stochastic order. (C) 2007 Elsevier B.V. All rights reserved
In a Bayesian framework, prior distributions on a space of nonparametric continuous distributions ma...
A family of nonparametric prior distributions which extends the Dirichlet process is introduced and ...
We consider Bayesian inference in the linear regression problem with an unknown error distribution t...
Random Bernstein polynomials which are also probability distribution functions on the closed unit in...
This paper introduces a new approach to Bayesian nonparametric inference for densities on the hyper...
We propose a Bayesian nonparametric procedure for density estimation, for data in a closed, bounded ...
Our focus is on constructing a multiscale nonparametric prior for densities. The Bayes density estim...
This paper considers a finite set of discrete distributions all having the same finite support. The ...
This book presents a systematic and comprehensive treatment of various prior processes that have bee...
The evaluation of the performance of a continuous diagnostic measure is a commonly encountered task ...
My dissertation considers three related topics involving censored or truncated survival data. All th...
This paper describes a general scheme for accomodating different types of conditional distributions ...
According to the Bayesian theory, observations are usually considered to be part of an infinite sequ...
I propose two new kernel-based models that enable an exact generative procedure: the Gaussian proces...
Pólya trees fix partitions and use random probabilities in order to construct random probability mea...
In a Bayesian framework, prior distributions on a space of nonparametric continuous distributions ma...
A family of nonparametric prior distributions which extends the Dirichlet process is introduced and ...
We consider Bayesian inference in the linear regression problem with an unknown error distribution t...
Random Bernstein polynomials which are also probability distribution functions on the closed unit in...
This paper introduces a new approach to Bayesian nonparametric inference for densities on the hyper...
We propose a Bayesian nonparametric procedure for density estimation, for data in a closed, bounded ...
Our focus is on constructing a multiscale nonparametric prior for densities. The Bayes density estim...
This paper considers a finite set of discrete distributions all having the same finite support. The ...
This book presents a systematic and comprehensive treatment of various prior processes that have bee...
The evaluation of the performance of a continuous diagnostic measure is a commonly encountered task ...
My dissertation considers three related topics involving censored or truncated survival data. All th...
This paper describes a general scheme for accomodating different types of conditional distributions ...
According to the Bayesian theory, observations are usually considered to be part of an infinite sequ...
I propose two new kernel-based models that enable an exact generative procedure: the Gaussian proces...
Pólya trees fix partitions and use random probabilities in order to construct random probability mea...
In a Bayesian framework, prior distributions on a space of nonparametric continuous distributions ma...
A family of nonparametric prior distributions which extends the Dirichlet process is introduced and ...
We consider Bayesian inference in the linear regression problem with an unknown error distribution t...