54 search results - page 2 / 11 » Dirichlet Process Gaussian Mixture Models: Choice of the Bas... |
ICASSP
2009
IEEE
13 years 8 months ago
2009
IEEE
The Dirichlet process can be used as a nonparametric prior for an infinite-dimensional probability mass function on the parameter space of a mixture model. The set of parameters o...
ICASSP
2011
IEEE
12 years 8 months ago
2011
IEEE
In this paper, we present a novel technique for modeling the posterior probability estimates obtained from a neural network directly in the HMM framework using the Dirichlet Mixtu...
JMLR
2010
12 years 11 months ago
2010
In this paper, we develop a novel online algorithm based on the Sequential Monte Carlo (SMC) samplers framework for posterior inference in Dirichlet Process Mixtures (DPM) (DelMor...
ICDM
2007
IEEE
13 years 11 months ago
2007
IEEE
In spite of the popularity of probabilistic mixture models for latent structure discovery from data, mixture models do not have a natural mechanism for handling sparsity, where ea...
ICML
2004
IEEE
14 years 5 months ago
2004
IEEE
Variational inference methods, including mean field methods and loopy belief propagation, have been widely used for approximate probabilistic inference in graphical models. While ...