ion for Stochastic Systems by Erlang's Method of Stages Joost-Pieter Katoen1 , Daniel Klink1 , Martin Leucker2 , and Verena Wolf3 1 RWTH Aachen University 2 TU Munich 3 EPF La...
Joost-Pieter Katoen, Daniel Klink, Martin Leucker,...
This paperconsidersthe problem of representingcomplex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic proce...
Abstract— In this paper, we consider a class of continuoustime, continuous-space stochastic optimal control problems. Building upon recent advances in Markov chain approximation ...
Current prediction models for usability evaluations are based on stochastic distributions derived from series of Bernoulli processes. The underlying assumption of these models is ...
In this paper, we present an automatic statistical approach for extracting 3D blood vessels from time-of-flight (TOF) magnetic resonance angiography (MRA) data. The voxels of the d...