Distributed Perception Networks (DPN) are a MAS approach to large scale fusion of heterogeneous and noisy information. DPN agents can establish meaningful information filtering c...
Inference methods for detecting attacks on information resources typically use signature analysis or statistical anomaly detection methods. The former have the advantage of attack...
Dynamic Bayesian networks (DBNs) offer an elegant way to integrate various aspects of language in one model. Many existing algorithms developed for learning and inference in DBNs ...
Abstract— We consider stochastic guarantees for networks with aggregate scheduling, in particular, Expedited Forwarding (EF). Our approach on the assumption that a node can be ab...
We characterize probabilities in Bayesian networks in terms of algebraic expressions called quasi-probabilities. These are arrived at by casting Bayesian networks as noisy AND-OR-...