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81
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UAI
2004
15 years 7 days ago
"Ideal Parent" Structure Learning for Continuous Variable Networks
In recent years, there is a growing interest in learning Bayesian networks with continuous variables. Learning the structure of such networks is a computationally expensive proced...
Iftach Nachman, Gal Elidan, Nir Friedman
86
Voted
NIPS
1997
15 years 7 days ago
Nonlinear Markov Networks for Continuous Variables
We address the problem of learning structure in nonlinear Markov networks with continuous variables. This can be viewed as non-Gaussian multidimensional density estimation exploit...
Reimar Hofmann, Volker Tresp
94
Voted
ICML
1996
IEEE
15 years 11 months ago
Discretizing Continuous Attributes While Learning Bayesian Networks
We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in th...
Moisés Goldszmidt, Nir Friedman
118
Voted
UAI
1998
15 years 7 days ago
A Multivariate Discretization Method for Learning Bayesian Networks from Mixed Data
In this paper we address the problem of discretization in the context of learning Bayesian networks (BNs) from data containing both continuous and discrete variables. We describe ...
Stefano Monti, Gregory F. Cooper
79
Voted
NIPS
1996
15 years 6 days ago
Continuous Sigmoidal Belief Networks Trained using Slice Sampling
Real-valued random hidden variables can be useful for modelling latent structure that explains correlations among observed variables. I propose a simple unit that adds zero-mean G...
Brendan J. Frey