33 search results - page 2 / 7 » Bayesian Inference for Linear Dynamic Models With Dirichlet ... |
JCST
2010
13 years 3 months ago
2010
In the Bayesian mixture modeling framework it is possible to infer the necessary number of components to model the data and therefore it is unnecessary to explicitly restrict the n...
PAMI
2010
13 years 3 months ago
2010
—We consider the problem of inferring and modeling topics in a sequence of documents with known publication dates. The documents at a given time are each characterized by a topic...
ICASSP
2011
IEEE
12 years 9 months ago
2011
IEEE
The Dirichlet Process Mixture (DPM) models represent an attractive approach to modeling latent distributions parametrically. In DPM models the Dirichlet process (DP) is applied es...
ECCV
2006
Springer
14 years 7 months ago
2006
Springer
A nonparametric Bayesian model for histogram clustering is proposed to automatically determine the number of segments when Markov Random Field constraints enforce smooth class assi...
AAAI
2010
13 years 6 months ago
2010
Matrix factorization is a fundamental technique in machine learning that is applicable to collaborative filtering, information retrieval and many other areas. In collaborative fil...