Probabilistic inference will be of special importance when one needs to know how much we can say with what all we know given new observations. Bayesian Network is a graphical prob...
We present further developments in our work on using data from real users to build a probabilistic model of user affect based on Dynamic Bayesian Networks (DBNs) and designed to de...
Abstract. This paper extends previously proposed bound propagation algorithm [11] for computing lower and upper bounds on posterior marginals in Bayesian networks. We improve the b...
: Previously we have proposed a theoretical framework, called BayesOWL, to model uncertainty in semantic web ontologies based on Bayesian networks. In particular, we have developed...
—Cooperation between intrusion detection systems (IDSs) allows collective information and experience from a network of IDSs to be shared to improve the accuracy of detection. A c...
Carol J. Fung, Quanyan Zhu, Raouf Boutaba, Tamer B...