Standard regression approaches assume that some finite number of the response
distribution characteristics, such as location and scale, change as a (parametric or …

For exchangeable data, mixture models are an extremely useful tool for density estimation
due to their attractive balance between smoothness and flexibility. When additional …

Standard clustering techniques assume a common configuration for all features in a dataset.
However, when dealing with multi-view or longitudinal data, the clusters' number …

Overweight and obesity in adults are known to be associated with increased risk of
metabolic and cardiovascular diseases. Obesity has now reached epidemic proportions …

We propose a nonparametric compound Poisson model for underreported count data that
introduces a latent clustering structure for the reporting probabilities. The latter are estimated …

We describe BayesMix, a C++ library for MCMC posterior simulation for general Bayesian
mixture models. The goal of BayesMix is to provide a self-contained ecosystem to perform …

We propose a methodology for modelling and comparing probability distributions within a
Bayesian nonparametric framework. Building on dependent normalised random measures …

We develop a nonparametric Bayesian modeling approach to ordinal regression based on
priors placed directly on the discrete distribution of the ordinal responses. The prior …

We present the AdaptSPEC-X method for the joint analysis of a panel of possibly
nonstationary time series. The approach is Bayesian and uses a covariate-dependent …

In several observational contexts where different raters evaluate a set of items, it is common
to assume that all raters draw their scores from the same underlying distribution. However, a …