The paper is an overview of a recently developed compilation data structure for graphical models, with specific application to constraint networks. The AND/OR Multi-Valued Decision...
Graphical structures such as Bayesian networks or Markov networks are very useful tools for representing irrelevance or independency relationships, and they may be used to e cientl...
The accuracy of a model to forecast a time series diminishes as the prediction horizon increases, in particular when the prediction is carried out recursively. Such decay is faster...
We consider the problem of learning Bayesian network models in a non-informative setting, where the only available information is a set of observational data, and no background kn...
Wireless ad-hoc networks consist of mobile nodes forming a dynamically changing topology without any infrastructure. Multicasting in a wireless ad-hoc network is difficult and chal...
George D. Kondylis, Srikanth V. Krishnamurthy, Son...