Background: Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more importa...
Bayesian models of multisensory perception traditionally address the problem of estimating an underlying variable that is assumed to be the cause of the two sensory signals. The b...
In this paper we investigate the fault diagnosis problem in IP networks. We provide a lower bound on the average number of probes per edge using variational inference technique pro...
Rajesh Narasimha, Souvik Dihidar, Chuanyi Ji, Stev...
Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approx...
Abstract. A localization algorithm using radio interferometric measurements is presented. A probabilistic model is constructed that accounts for general noise models and lends itse...
Dennis Lucarelli, Anshu Saksena, Ryan Farrell, I-J...