The use of microarray data has become quite commonplace in medical and scientific experiments. We focus here on microarray data generated from cancer studies. It is potentially important for the discovery of biomarkers to identify genes whose expression levels correlate with tumor progression....

In recent years the Dirichlet process prior has experienced a great success in the context of Bayesian mixture modelling. The idea of overcoming discreteness of its realizations by exploiting it in hierarchical models, combined with the development of suitable sampling techniques, represent one...

This paper explores the patterns of change in the annual household income in the countries of the European Community during the years 1994-1999. The income is modelled by mixtures of multivariate log-normal distributions, and the mixture components are interpreted as representing one...

This paper investigates heterogeneity in the preferences/WTP (willingness to pay) to preserve marble monuments in Washington, D.C. This is done in the context of three different discrete-choice random-utility models. The main focus is to estimate a mixture model of choices over preservation...

This paper is concerned with simulation-based inference in generalized models of stochastic volatility defined by heavy-tailed Student-t distributions (with unknown degrees of freedom) and exogenous variables in the observation and volatility equations and a jump component in the observation...