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NIPS
2004
13 years 9 months ago
Unsupervised Variational Bayesian Learning of Nonlinear Models
In this paper we present a framework for using multi-layer perceptron (MLP) networks in nonlinear generative models trained by variational Bayesian learning. The nonlinearity is h...
Antti Honkela, Harri Valpola
KBS
2006
231views more  KBS 2006»
13 years 7 months ago
Predicting football results using Bayesian nets and other machine learning techniques
Bayesian networks (BNs) provide a means for representing, displaying, and making available in a usable form the knowledge of experts in a given Weld. In this paper, we look at the...
A. Joseph, Norman E. Fenton, Martin Neil
AAAI
2006
13 years 9 months ago
Solving MAP Exactly by Searching on Compiled Arithmetic Circuits
The MAP (maximum a posteriori hypothesis) problem in Bayesian networks is to find the most likely states of a set of variables given partial evidence on the complement of that set...
Jinbo Huang, Mark Chavira, Adnan Darwiche
IPSN
2010
Springer
14 years 2 months ago
Bayesian optimization for sensor set selection
We consider the problem of selecting an optimal set of sensors, as determined, for example, by the predictive accuracy of the resulting sensor network. Given an underlying metric ...
Roman Garnett, Michael A. Osborne, Stephen J. Robe...
BMCBI
2010
97views more  BMCBI 2010»
13 years 2 months ago
A semi-parametric Bayesian model for unsupervised differential co-expression analysis
Background: Differential co-expression analysis is an emerging strategy for characterizing disease related dysregulation of gene expression regulatory networks. Given pre-defined ...
Johannes M. Freudenberg, Siva Sivaganesan, Michael...