109 search results - page 2 / 22 » Bayesian Model Selection when the Number of Components is Un... |
PAMI
2006
13 years 5 months ago
2006
We present a Bayesian method for mixture model training that simultaneously treats the feature selection and the model selection problem. The method is based on the integration of ...
TSP
2008
13 years 5 months ago
2008
This paper proposes a hierarchical Bayesian model that can be used for semi-supervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linea...
TSP
2010
13 years 7 hour ago
2010
We propose a variance-component probabilistic model for sparse signal reconstruction and model selection. The measurements follow an underdetermined linear model, where the unknown...
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...
CORR
2006
Springer
13 years 5 months ago
2006
Springer
We derive an exact and efficient Bayesian regression algorithm for piecewise constant functions of unknown segment number, boundary location, and levels. It works for any noise an...