Bayesian networks with mixtures of truncated exponentials (MTEs) support efficient inference algorithms and provide a flexible way of modeling hybrid domains (domains containing ...
Helge Langseth, Thomas D. Nielsen, Rafael Rum&iacu...
Credal nets generalize Bayesian nets by relaxing the requirement of precision of probabilities. Credal nets are considerably more expressive than Bayesian nets, but this makes bel...
Alessandro Antonucci, Yi Sun, Cassio P. de Campos,...
Geospatial Reasoning has been an essential aspect of military planning since the invention of cartography. Although maps have always been a focal point for developing situational ...
Kathryn B. Laskey, Edward J. Wright, Paulo Cesar G...
In the last 10 years, there has been increasing interest in interval valued data in signal processing. According to the conventional view, an interval value supposedly reflects th...
The basic idea of an algebraic approach to learning Bayesian network (BN) structures is to represent every BN structure by a certain uniquely determined vector, called the standar...