We describe scoring metrics for learning Bayesian networks from a combination of user knowledge and statistical data. We identify two important properties of metrics, which we cal...
David Heckerman, Dan Geiger, David Maxwell Chicker...
We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-...
Gene network reconstruction is a multidisciplinary research area involving data mining, machine learning, statistics, ontologies and others. Reconstructed gene network allows us t...
Design and development of novel human-computer interfaces poses a challenging problem: actions and intentions of users have to be inferred from sequences of noisy and ambiguous mu...
Vladimir Pavlovic, James M. Rehg, Ashutosh Garg, T...
When a large amount of data are missing, or when multiple hidden nodes exist, learning parameters in Bayesian networks (BNs) becomes extremely difficult. This paper presents a lea...