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 …

The paper deals with the problem of determining the number of components in a mixture
model. We take a Bayesian non-parametric approach and adopt a hierarchical model with a …

In recent years the Dirichlet process prior has experienced a great success in the context of
Bayesian mixture modeling. The idea of overcoming discreteness of its realizations by …

Exponential functionals of Le19 evy processes Page 1 Probability Surveys Vol. 2 (2005) 191–212
ISSN: 1549-5787 DOI: 10.1214/154957805100000122 Exponential functionals of Lévy …

Recently the class of normalized random measures with independent increments, which
contains the Dirichlet process as a particular case, has been introduced. Here a new …

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

Neutral to the right (NTR) processes were introduced by Doksum in 1974 as Bayesian priors
on the class of distributions on the real line. Since that time there have been numerous …

Completely random measures (CRM) represent the key building block of a wide variety of
popular stochastic models and play a pivotal role in modern Bayesian Nonparametrics. The …

This paper considers a generalization of the Dirichlet process which is obtained by suitably
normalizing superposed independent gamma processes having increasing integer-valued …

Bayesian nonparametric inferential procedures based on Markov chain Monte Carlo
marginal methods typically yield point estimates in the form of posterior expectations …