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...
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...
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...
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...
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...