Scale mixtures of uniform distributions are used to model non-normal data in both univariate and multivariate settings. In addition to providing greater modelling flexibility, the use of scale mixtures of uniforms also results in straightforward computational strategies, particularly in a...

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-trucated and interval censored data, with and without covariates, in survival models. The models and methods...

We discuss the relevance of consistency to the Bayesian. Should consistency be dismissed as irrelevant or thought about seriously when constructing prior distributions? Strong opinions have been held on this matter, but it is probably fair to say it is a largely neglected area. Pioneers, such as...

An approach to constructing strictly stationary AR(1)-type models with arbitrary stationary distributions and a flexible dependence structure is introduced. Bayesian nonparametric predictive density functions, based on single observations, are used to construct the one-step ahead predictive...

We define and investigate a new class of measure-valued Markov chains by resorting to ideas formulated in Bayesian nonparametrics related to the Dirichlet process and the Gibbs sampler. Dependent random probability measures in this class are shown to be stationary and ergodic with respect to the...