This paper proposes a dynamic model supporting multimodal state space probability distributions and presents the application of the model in dealing with visual occlusions when tr...
Naive Bayes models have been widely used for clustering and classification. However, they are seldom used for general probabilistic learning and inference (i.e., for estimating an...
Abstract. We present a formal system that models programmable abstractions for access control. Composite abstractions and patterns of arbitrary complexity are modeled as a configu...
Stationarity is often an unrealistic prior assumption for Gaussian process regression. One solution is to predefine an explicit nonstationary covariance function, but such covaria...
Tailoring solver-independent constraint instances to target solvers is an important component of automated constraint modelling. We augment the tailoring process by a set of enhan...
Andrea Rendl, Ian Miguel, Ian P. Gent, Christopher...