Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
We compare two approaches to Bayesian network inference, called variable elimination (VE) and arc reversal (AR). It is established that VE never requires more space than AR, and n...
Cory J. Butz, Junying Chen, Ken Konkel, Pawan Ling...
Markov jump processes and continuous time Bayesian networks are important classes of continuous time dynamical systems. In this paper, we tackle the problem of inferring unobserve...
Gene network reconstruction is a multidisciplinary research area involving data mining, machine learning, statistics, ontologies and others. Reconstructed gene network allows us t...
Cognitive networking deals with applying cognition to the entire network protocol stack for achieving stack-wide as well as network-wide performance goals, unlike cognitive radios ...
Giorgio Quer, Hemanth Meenakshisundaram, Tamma Bhe...