Add to ORKGhttp://deepblue.lib.umich.edu/bitstream/2027.42/36227/2/b1908133.0001.001.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/36227/1/b1908133.0001.001.tx
La thèse est divisée en deux parties portant sur deux aspects relativement différents des approches ...
l Statistical inference concerns unknown parameters that describe certain population characteristics...
Bayesian Statistics has been increasingly popular in the last five decades. Besides having decision ...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73242/1/1467-9868.00190.pd
The paper evaluates the usefulness of a nonparametric approach to Bayesian inference by presenting t...
This paper considers the problem of reporting a "posterior distribution" using a parametric family o...
Prior specification for nonparametric Bayesian inference involves the difficult task of quan-tifying...
Nonparametric Bayesian inference has widespread applications in statistics and machine learning. In ...
Bayesian nonparametric inference, Dirichlet process, generalized gamma convolutions, Lauricella hype...
The Bayesian nonparametric inference requires the construction of priors on infinite dimensional spa...
http://deepblue.lib.umich.edu/bitstream/2027.42/36229/2/b1893026.0001.001.pdfhttp://deepblue.lib.umi...
In this talk I will discuss some recent progress in Bayesian nonparametric modeling and inference. ...
PhDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib...
The definition and investigation of general classes of non-parametric priors has recently been an ac...
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 ...
l Statistical inference concerns unknown parameters that describe certain population characteristics...
Bayesian Statistics has been increasingly popular in the last five decades. Besides having decision ...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73242/1/1467-9868.00190.pd
The paper evaluates the usefulness of a nonparametric approach to Bayesian inference by presenting t...
This paper considers the problem of reporting a "posterior distribution" using a parametric family o...
Prior specification for nonparametric Bayesian inference involves the difficult task of quan-tifying...
Nonparametric Bayesian inference has widespread applications in statistics and machine learning. In ...
Bayesian nonparametric inference, Dirichlet process, generalized gamma convolutions, Lauricella hype...
The Bayesian nonparametric inference requires the construction of priors on infinite dimensional spa...
http://deepblue.lib.umich.edu/bitstream/2027.42/36229/2/b1893026.0001.001.pdfhttp://deepblue.lib.umi...
In this talk I will discuss some recent progress in Bayesian nonparametric modeling and inference. ...
PhDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib...
The definition and investigation of general classes of non-parametric priors has recently been an ac...
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 ...
l Statistical inference concerns unknown parameters that describe certain population characteristics...
Bayesian Statistics has been increasingly popular in the last five decades. Besides having decision ...