This paper considers generating exchangeable partition probability functions from an independent and identically distributed sample from a geometric distribution. We show that the model is rich and while different from exchangeable random variables based on nonparametric models, such as the...

This article investigates the problem of Bayesian nonparametric regression. The proposed model is based on a recently introduced random distribution function, which is based on a decreasing set of weights. The approach is surprisingly of a much simpler form than alternative models described in...

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...

In this work we propose a Bayesian nonparametric approach for tackling statistical problems related to EST surveys. In particular, we provide estimates for: a) the coverage, defined as the proportion of unique genes in the library represented in the given sample of reads; b) the number of new...

When dealing with risk models the typical assumption of independence among claim size distributions is not always satisfied. Here we consider the case when the claim sizes are exchangeable and study the implications when constructing aggregated claims through compound Poisson type processes. In...

Discrete random probability measures and the exchangeable random partitions they induce are key tools for addressing a variety of estimation and prediction problems in Bayesian inference. Indeed, many popular nonparametric priors, such as the Dirichlet and the Pitman–Yor process priors, select...

High-dimensional spectroscopy data are increasingly common in many fields of science. Building classification models in this context is challenging, due not only to high dimensionality but also to high autocorrelations. A two-stage classification strategy is proposed. First, in a data...

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...

A Bayesian non-parametric methodology has been recently proposed to deal with the issue of prediction within species sampling problems. Such problems concern the evaluation, conditional on a sample of size "n", of the species variety featured by an additional sample of size "m". Genomic...

A Bayesian nonparametric methodology has been recently proposed in order to deal with the issue of prediction within species sampling problems. Such problems concern the evaluation, conditional on a sample of size n, of the species variety featured by an additional sample of size m. Genomic...