Empirische Inferenz
Talk
2006
MCMC inference in (Conditionally) Conjugate Dirichlet Process Gaussian Mixture Models
We compare the predictive accuracy of the Dirichlet Process Gaussian mixture models using conjugate and conditionally conjugate priors and show that better density models result from using the wider class of priors. We explore several MCMC schemes exploiting conditional conjugacy and show their computational merits on several multidimensional density estimation problems.
| Author(s): | Rasmussen, CE. and Görür, D. |
| Year: | 2006 |
| Month: | June |
| Day: | 0 |
| Bibtex Type: | Talk (talk) |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Event Name: | ICML Workshop on Learning with Nonparametric Bayesian Methods 2006 |
| Event Place: | Pittsburgh, PA, USA |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@talk{5365,
title = {MCMC inference in (Conditionally) Conjugate Dirichlet Process Gaussian Mixture Models},
abstract = {We compare the predictive accuracy of the Dirichlet Process Gaussian mixture models using conjugate and conditionally conjugate priors and show that better density models result from using the wider class of priors. We explore several MCMC schemes exploiting conditional conjugacy and show their computational merits on several multidimensional density estimation problems.},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
month = jun,
year = {2006},
slug = {5365},
author = {Rasmussen, CE. and G{\"o}r{\"u}r, D.},
month_numeric = {6}
}