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» Bayesian Approaches to Gaussian Mixture Modeling
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DSP
2007
13 years 9 months ago
Variational and stochastic inference for Bayesian source separation
We tackle the general linear instantaneous model (possibly underdetermined and noisy) where we model the source prior with a Student t distribution. The conjugate-exponential char...
Ali Taylan Cemgil, Cédric Févotte, S...
ICPR
2002
IEEE
14 years 10 months ago
Visual Abstraction of Wildlife Footage Using Gaussian Mixture Models and the Minimum Description Length Criterion
bstraction of Wildlife Footage using Gaussian Mixture Models and the Minimum Description Length Criterion David Gibson Neill Campbell Barry Thomas Department of Computer Science Un...
David P. Gibson, Neill W. Campbell, Barry T. Thoma...
SSPR
2010
Springer
13 years 7 months ago
Non-parametric Mixture Models for Clustering
Mixture models have been widely used for data clustering. However, commonly used mixture models are generally of a parametric form (e.g., mixture of Gaussian distributions or GMM),...
Pavan Kumar Mallapragada, Rong Jin, Anil K. Jain
PIMRC
2008
IEEE
14 years 3 months ago
Bayesian inference in linear models with a random Gaussian matrix : Algorithms and complexity
—We consider the Bayesian inference of a random Gaussian vector in a linear model with a random Gaussian matrix. We review two approaches to finding the MAP estimator for this m...
Ido Nevat, Gareth W. Peters, Jinhong Yuan
BMCBI
2007
172views more  BMCBI 2007»
13 years 9 months ago
Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks
Background: Reverse engineering cellular networks is currently one of the most challenging problems in systems biology. Dynamic Bayesian networks (DBNs) seem to be particularly su...
Fulvia Ferrazzi, Paola Sebastiani, Marco Ramoni, R...