Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Originally devoted to specific applications such as biology, medicine and demography, duration models are now widely used in economy, finance or reliability. Recent works in var...
Roland Donat, Philippe Leray, Laurent Bouillaut, P...
Researchers have previously looked into the problem of determining if a given set of security hardening measures can effectively make a networked system secure. Many of them also...
In this work we present a novel multi-modal mixed-state dynamic Bayesian network (DBN) for robust meeting event classification. The model uses information from lapel microphones,...
In this paper we present a family of algorithms for estimating stream weights for dynamic Bayesian networks with multiple observation streams. For the 2 stream case, we present a ...