Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image ...
Abstract. We develop an algorithm to compute timed reachability probabilities for distributed models which are both probabilistic and nondeterministic. To obtain realistic results ...
Georgel Calin, Pepijn Crouzen, Pedro R. D'Argenio,...
Abstract. Real-world optimization problems are often subject to uncertainties, which can arise regarding stochastic model parameters, objective functions and decision variables. Th...
—Adapting the hyperparameters of support vector machines (SVMs) is a challenging model selection problem, especially when flexible kernels are to be adapted and data are scarce....
Abstract-- This paper considers a recently proposed framework for experiment design in system identification for control. We will consider model based control design methods, such ...