Dirichlet processes (DP) are widely applied in Bayesian nonparametric modeling. However,
in their basic form they do not directly integrate dependency information among data arising …

The distance dependent Chinese restaurant process (ddCRP) provides a flexible framework
for clustering data with temporal, spatial, or other structured dependencies. Here we model …

Mixture models, or convex combinations of a countable number of probability distributions,
offer an elegant framework for inference when the population of interest can be subdivided …

Dirichlet processes and their extensions have reached a great popularity in Bayesian
nonparametric statistics. They have also been introduced for spatial and spatio-temporal …

We consider the problem of analyzing the heterogeneity of clustering distributions for
multiple groups of observed data, each of which is indexed by a covariate value, and …

In certain biomedical studies, one may anticipate changes in the shape of a response
distribution across the levels of an ordinal predictor. For instance, in toxicology studies …

In Bayesian hierarchical modeling, it is often appealing to allow the conditional density of an
(observable or unobservable) random variable Y to change flexibly with categorical and …

In this paper we investigate the stick-breaking representation for the class of σ-stable
Poisson-Kingman models, also known as Gibbs-type random probability measures. This …

Side information, or auxiliary information associated with documents or image content,
provides hints for clustering. We propose a new model, side information dependent Chinese …

Median and quantiles are robust alternatives of moments-based estimators, like mean and
covariance. In higher dimensions, there is no objective notion of ordering, and as a …