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We propose a general Bayesian network model for application in a wide class of problems of therapy monitoring. We discuss the use of stochastic simulation as a computational approach to inference on t...
A maximum likelihood based model selection of discrete Bayesian networks is considered. The model selection is performed through scoring function S, which, for a given network G and n-sample Dn, is ...
The estimation of Bayesian networks given high-dimensional data sets, in particular given gene expression data sets, has been the focus of much recent research. While there are many methods availabl...
Conditional independence models in the Gaussian case are algebraic varieties in the cone of positive definite covariance matrices. We study these varieties in the case of Bayesian networks, with a v...

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