Modern Bayesian Network learning algorithms are timeefficient, scalable and produce high-quality models; these algorithms feature prominently in decision support model development...
In this paper we consider the problem of testing whether a graph is triangle-free, and more generally, whether it is H-free, for a fixed subgraph H. The algorithm should accept gr...
Noga Alon, Tali Kaufman, Michael Krivelevich, Dana...
Many network applications that require Quality-ofService QoS support, such as transmission of digital voice and video, tolerate a certain level of service degradation. In this s...
Abstract—The following network computing problem is considered. Source nodes in a directed acyclic network generate independent messages and a single receiver node computes a tar...
Robust Bayesian inference is the calculation of posterior probability bounds given perturbations in a probabilistic model. This paper focuses on perturbations that can be expresse...