We review the basic principles of Quasi-Monte Carlo (QMC) methods, the randomizations that turn them into variancereduction techniques, and the main classes of constructions under...
We present an approach for applying symmetry reduction techniques to probabilistic model checking, a formal verification method for the quantitative analysis of systems with stocha...
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
We present a novel framework for learning a joint shape and appearance model from a large set of un-labelled training examples in arbitrary positions and orientations. The shape an...
The verification of quantitative aspects like performance and dependability by means of model checking has become an important and vivid area of research over the past decade. An ...
Stefan Blom, Boudewijn R. Haverkort, Matthias Kunt...