Bayesian inference methods are commonly applied to the classification of brain Magnetic Resonance images (MRI). We use the Maximum Evidence (ME) approach to estimate the most prob...
We propose a modification of the dynamic neural field model of Amari [1], aiming at reducing the simulation effort by employing spaceand frequency representations of the dynamic st...
Alexander Gepperth, Jannik Fritsch, Christian Goer...
Ethernet is the most widely used network and many analytical models were developed to predict its capacity. Usually in these models assumptions like infinite population, no back-of...
We describe a language which can be used to model business processes (both technical and nontechnical). It has a formal semantics, so as to enable formal analysis and prediction o...
— This paper proposes a low-complexity model for vertical antenna radiation patterns, e.g. for inclusion in systemlevel simulations. They can be seen as extensions to the horizon...
Fredrik Gunnarsson, Martin N. Johansson, Anders Fu...