Iterated random functions are used to draw pictures or simulate large Ising models, among
other applications. They offer a method for studying the steady state distribution of a Markov …

Markov Chains and Stochastic Stability is part of the Communications and Control
Engineering Series (CCES) edited by Professors BW Dickinson, ED Sontag, M. Thoma, A …

We consider the problem of determining the distribution of means of random probability
measures which are obtained by normalizing increasing additive processes. A solution is …

In Section 1, we present a number of classical results concerning the Generalized Gamma
Convolution (: GGC) variables, their Wiener-Gamma representations, and relation with the …

The distribution of a mean or, more generally, of a vector of means of a Dirichlet process is
considered. Some characterizing aspects of this paper are:(i) a review of a few basic results …

This paper introduces and studies a new class of nonparametric prior distributions. Random
probability distribution functions are constructed via normalization of random measures …

Data analysis sometimes requires the relaxation of parametric assumptions in order to gain
modeling flexibility and robustness against mis-specification of the probability model. In the …

The Lauricella theory of multiple hypergeometric functions is used to shed some light on
certain distributional properties of the mean of a Dirichlet process. This approach leads to …

Air quality monitoring is based on pollutants concentration levels, typically recorded in
metropolitan areas. These exhibit spatial and temporal dependence as well as seasonality …

We propose a new class of time dependent random probability measures and show how this
can be used for Bayesian nonparametric inference in continuous time. By means of a …