An important class of continuous Bayesian networks are those that have linear conditionally deterministic variables (a variable that is a linear deterministic function of its pare...
We propose a practical technique for the identification of lossy network links from end-to-end measurements. Our scheme is based on a function that computes the likelihood of each...
We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and wh...
Inferring transcriptional regulatory networks from geneexpression data remains a challenging problem, in part because of the noisy nature of the data and the lack of strong networ...
Abstract-- This study deals with investigating the classification performance of information-theoretic measures when applied to complex biological networks. In particular, our aim ...
Laurin A. J. Mueller, Karl G. Kugler, Andreas Dand...