Add to ORKGIn this talk I will discuss some recent progress in Bayesian nonparametric modeling and inference. Focusing on the needs of applications, I will discuss some of the limitations of the popular Dirichlet process, in particular its lack of power-law marginals and its poor scaling in problems involving large numbers of clusters. I will present some alternatives that are based on the theory of completely random measures and on stick-breaking constructions. I will present applications to problems in computational vision and bioinformatics. [Joint work with Erik Sudderth and Romain Thibaux.
This article is concerned with nonparametric inference for quantiles from a Bayesian perspective, us...
In recent years, there has been increasing interest in Bayesian nonparametric methods for high-dimen...
Bayesian nonparametric inference, Dirichlet process, generalized gamma convolutions, Lauricella hype...
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 availability of complex-structured data has sparked new research directions in statistics and ma...
<p>The dissertation focuses on solving some important theoretical and methodological problems associ...
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...
La thèse est divisée en deux parties portant sur deux aspects relativement différents des approches ...
Nonparametric Bayesian models are commonly used to obtain robust statistical inference, and the most...
The dissertation focuses on solving some important theoretical and methodological problems associate...
The definition and investigation of general classes of non-parametric priors has recently been an ac...
Modelling for spatially referenced data is receiving increased attention in the statistics and the m...
This article is concerned with nonparametric inference for quantiles from a Bayesian perspective, us...
In recent years, there has been increasing interest in Bayesian nonparametric methods for high-dimen...
Bayesian nonparametric inference, Dirichlet process, generalized gamma convolutions, Lauricella hype...
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 availability of complex-structured data has sparked new research directions in statistics and ma...
<p>The dissertation focuses on solving some important theoretical and methodological problems associ...
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...
La thèse est divisée en deux parties portant sur deux aspects relativement différents des approches ...
Nonparametric Bayesian models are commonly used to obtain robust statistical inference, and the most...
The dissertation focuses on solving some important theoretical and methodological problems associate...
The definition and investigation of general classes of non-parametric priors has recently been an ac...
Modelling for spatially referenced data is receiving increased attention in the statistics and the m...
This article is concerned with nonparametric inference for quantiles from a Bayesian perspective, us...
In recent years, there has been increasing interest in Bayesian nonparametric methods for high-dimen...
Bayesian nonparametric inference, Dirichlet process, generalized gamma convolutions, Lauricella hype...