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» On Bayesian model and variable selection using MCMC
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CSDA
2010
208views more  CSDA 2010»
14 years 9 months ago
Bayesian density estimation and model selection using nonparametric hierarchical mixtures
We consider mixtures of parametric densities on the positive reals with a normalized generalized gamma process (Brix, 1999) as mixing measure. This class of mixtures encompasses t...
Raffaele Argiento, Alessandra Guglielmi, Antonio P...
GECCO
2007
Springer
314views Optimization» more  GECCO 2007»
15 years 3 months ago
Variable selection for wind power prediction using particle swarm optimization
Wind energy has an increasing influence on the energy supply in many countries, but in contrast to conventional power plants it is a fluctuating energy source. For its integration...
René Jursa
ICASSP
2011
IEEE
14 years 1 months ago
A Bernoulli-Gaussian model for gene factor analysis
This paper investigates a Bayesian model and a Markov chain Monte Carlo (MCMC) algorithm for gene factor analysis. Each sample in the dataset is decomposed as a linear combination...
Cecile Bazot, Nicolas Dobigeon, Jean-Yves Tournere...
CSDA
2010
194views more  CSDA 2010»
14 years 9 months ago
A clipped latent variable model for spatially correlated ordered categorical data
We propose a model for a point-referenced spatially correlated ordered categorical response and methodology for estimation of model parameters. Models and methods for spatially co...
Megan Dailey Higgs, Jennifer A. Hoeting
105
Voted
UAI
1998
14 years 11 months ago
The Bayesian Structural EM Algorithm
In recent years there has been a flurry of works on learning Bayesian networks from data. One of the hard problems in this area is how to effectively learn the structure of a beli...
Nir Friedman