Add to ORKGAbstract. This paper considers a generalization of the Dirichlet process which is obtained by suitably normalizing superposed independent gamma processes having increasing inte-ger-valued scale parameter. A comprehensive treatment of this random probability mea-sure is provided. We prove results concerning its finite-dimensional distributions, moments, predictive distributions and the distribution of its mean. Most expressions are given in terms of multiple hypergeometric functions, thus highlighting the interplay between Bayes-ian Nonparametrics and special functions. Finally, a suitable simulation algorithm is applied in order to compute quantities of statistical interest
The definition and investigation of general classes of non-parametric priors has recently been an ac...
AbstractA class of random processes with invariant sample paths, that is, processes which yield (wit...
In this paper we investigate a recently introduced class of nonparametric priors, termed generalize...
This paper considers a generalization of the Dirichlet process which is obtained by suitably normali...
Bayesian nonparametric inference, Dirichlet process, generalized gamma convolutions, Lauricella hype...
A family of nonparametric prior distributions which extends the Dirichlet process is introduced and ...
The Bayesian nonparametric inference requires the construction of priors on infinite dimensional spa...
The authors consider various sum-representations for the Dirichlet process when considered as a rand...
Bayesian nonparametric inference is a relatively young area of research and it has recently undergon...
This book presents a systematic and comprehensive treatment of various prior processes that have bee...
Increasing additive processes (IAP), i.e. processes with positive independent increments, represent ...
An approach to modeling dependent nonparametric random density functions is presented. This is based...
Dependent Dirichlet processes (DPs) are dependent sets of random measures, each being marginally DP ...
Recently the class of normalized random measures with independent increments has been introduced. Su...
This book focuses on the properties associated with the Dirichlet process, describing its use a prio...
The definition and investigation of general classes of non-parametric priors has recently been an ac...
AbstractA class of random processes with invariant sample paths, that is, processes which yield (wit...
In this paper we investigate a recently introduced class of nonparametric priors, termed generalize...
This paper considers a generalization of the Dirichlet process which is obtained by suitably normali...
Bayesian nonparametric inference, Dirichlet process, generalized gamma convolutions, Lauricella hype...
A family of nonparametric prior distributions which extends the Dirichlet process is introduced and ...
The Bayesian nonparametric inference requires the construction of priors on infinite dimensional spa...
The authors consider various sum-representations for the Dirichlet process when considered as a rand...
Bayesian nonparametric inference is a relatively young area of research and it has recently undergon...
This book presents a systematic and comprehensive treatment of various prior processes that have bee...
Increasing additive processes (IAP), i.e. processes with positive independent increments, represent ...
An approach to modeling dependent nonparametric random density functions is presented. This is based...
Dependent Dirichlet processes (DPs) are dependent sets of random measures, each being marginally DP ...
Recently the class of normalized random measures with independent increments has been introduced. Su...
This book focuses on the properties associated with the Dirichlet process, describing its use a prio...
The definition and investigation of general classes of non-parametric priors has recently been an ac...
AbstractA class of random processes with invariant sample paths, that is, processes which yield (wit...
In this paper we investigate a recently introduced class of nonparametric priors, termed generalize...