Sensors-to-sink data in wireless sensor networks (WSNs) are typically correlated with each other. Exploiting such correlation when performing data aggregation can result in consid...
Yujie Zhu, Ramanuja Vedantham, Seung-Jong Park, Ra...
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computat...
Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller,...
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Advances in wireless communications, positioning technology, and other hardware technologies combine to enable a range of applications that use a mobile user's geo-spatial da...
Laurynas Speicys, Christian S. Jensen, Augustas Kl...
In this paper we address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different c...