This paper considers the problem of Bayesian inference in dynamical models with time-varying dimension. These models have been studied in the context of multiple target tracking pr...
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 ...
Anomaly detection is a first and important step needed to respond to unexpected problems and to assure high performance and security in IP networks. We introduce a framework and ...
Yin Zhang, Zihui Ge, Albert G. Greenberg, Matthew ...
— Although efficient processing of probabilistic databases is a well-established field, a wide range of applications are still unable to benefit from these techniques due to t...
— In this paper, we propose a game theoretic approach to tackle the problem of the distributed formation of the hierarchical network architecture that connects the nodes in the u...