Bayesian nonparametric inference, Dirichlet process, generalized gamma convolutions, Lauricella hypergeometric functions, means of random probability measures, predictive distributions, 62F15, 60G57,
In this paper we investigate a recently introduced class of nonparametric priors, termed generalize...
We propose a novel Bayesian nonparametric process prior for modeling a collection of random discrete...
The availability of complex-structured data has sparked new research directions in statistics and ma...
This paper considers a generalization of the Dirichlet process which is obtained by suitably normali...
Abstract. This paper considers a generalization of the Dirichlet process which is obtained by suitab...
A family of nonparametric prior distributions which extends the Dirichlet process is introduced and ...
This book presents a systematic and comprehensive treatment of various prior processes that have bee...
Abstract. Bayesian nonparametric inference is a relatively young area of research and it has recentl...
Nonparametric Bayesian inference has widespread applications in statistics and machine learning. In ...
The Bayesian nonparametric inference requires the construction of priors on infinite dimensional spa...
The definition and investigation of general classes of non-parametric priors has recently been an ac...
Dirichlet process mixing, linear functionals, Monte Carlo sampling and integration, semiparametric m...
This article is concerned with nonparametric inference for quantiles from a Bayesian perspective, us...
http://deepblue.lib.umich.edu/bitstream/2027.42/36227/2/b1908133.0001.001.pdfhttp://deepblue.lib.umi...
Alternatives to the Dirichlet prior for multinomial probabilities are explored. The Dirichlet prior ...
In this paper we investigate a recently introduced class of nonparametric priors, termed generalize...
We propose a novel Bayesian nonparametric process prior for modeling a collection of random discrete...
The availability of complex-structured data has sparked new research directions in statistics and ma...
This paper considers a generalization of the Dirichlet process which is obtained by suitably normali...
Abstract. This paper considers a generalization of the Dirichlet process which is obtained by suitab...
A family of nonparametric prior distributions which extends the Dirichlet process is introduced and ...
This book presents a systematic and comprehensive treatment of various prior processes that have bee...
Abstract. Bayesian nonparametric inference is a relatively young area of research and it has recentl...
Nonparametric Bayesian inference has widespread applications in statistics and machine learning. In ...
The Bayesian nonparametric inference requires the construction of priors on infinite dimensional spa...
The definition and investigation of general classes of non-parametric priors has recently been an ac...
Dirichlet process mixing, linear functionals, Monte Carlo sampling and integration, semiparametric m...
This article is concerned with nonparametric inference for quantiles from a Bayesian perspective, us...
http://deepblue.lib.umich.edu/bitstream/2027.42/36227/2/b1908133.0001.001.pdfhttp://deepblue.lib.umi...
Alternatives to the Dirichlet prior for multinomial probabilities are explored. The Dirichlet prior ...
In this paper we investigate a recently introduced class of nonparametric priors, termed generalize...
We propose a novel Bayesian nonparametric process prior for modeling a collection of random discrete...
The availability of complex-structured data has sparked new research directions in statistics and ma...
DOI
Add to ORKG