This paper develops a novel approach studying the distribution of the regional population density across space. We work in a Bayesian parametric framework. Exploiting the Gamma distribution, we are able to introduce heterogeneity across space without incurring any a priori definition of...

This study proposes to investigate the effectiveness of modeling local spatial dependence through a conditionally autoregressive process (CAR) to picture the population distribution across space. Following the current literature, the idea is to model individual location preferences by focusing...

One of the main research areas in Bayesian Nonparametrics is the proposal and study of priors which generalize the Dirichlet process. Here we exploit theoretical properties of Poisson random measures in order to provide a comprehensive Bayesian analysis of random probabilities which are obtained...

Bayesian nonparametric inference is a relatively young area of research and it has recently undergone a strong development. Most of its success can be explained by the considerable degree of exibility it ensures in statistical modelling, if compared to parametric alternatives, and by the...

The present paper provides a review of the results concerning distributional properties of means of random probability measures. Our interest in this topic has originated from inferential problems in Bayesian Nonparametrics. Nonetheless, it is worth noting that these random quantities lay an...