The past decade has seen a remarkable development in the area of Bayesian nonparametric inference both from a theoretical and applied perspective. As for the latter, the celebrated Dirichlet process has been successfully exploited within Bayesian mixture models leading to many interesting...

In this paper we show that particular Gibbs sampler Markov processes can be modified to an autoregressive Markov process. The procedure allows the easy derivation of the innovation variables which provide strictly stationary autoregressive processes with fixed marginals. In particular, we...

We introduce approaches to performing Bayesian nonparametric statistical inference for distribution functions exhibiting a stochastic ordering. We consider Pólya tree prior distributions, and Bernstein polynomial prior distributions, and each prior provides an appealing and simple way of...

A Bayesian nonparametric approach to modeling a nonlinear dynamic model is presented. New techniques for sampling infinite mixture models are used. The inference procedure specifically in the case of the logistic model and when the nonparametric component is applied to the additive errors is...

A new simulation method, "auxiliary random functions" is introduced. When used within a Gibbs sampler, this method enables a unified treatment of exact, right-censored, left-censored, left-truncated and interval censored data, with and without covariates in survival models. The models and...