When we use simulation to evaluate the performance of a stochastic system, the simulation often contains input distributions estimated from real-world data; therefore, there is both simulation and input uncertainty in the performance estimates. Ignoring either source of uncertainty underestimates the overall statistical error. Simulation uncertainty can be reduced by additional computation (e.g...

We present a complete statistical analysis of quantum optical measurement schemes based on photodetection. Statistical distributions of quantum observables determined from a finite number of experimental runs are characterized with the help of the generating function, which we derive using the exact statistical description of raw experimental outcomes. We use the developed formalism to point ou...

In telecommunication networks, a common measure is the maximum congestion (i.e., utilisation) on edge capacity. As traffic demands are often known with a degree of uncertainty, network management techniques must take into account traffic variability. The oblivious performance of a routing is a measure of how congested the network may get, in the worst case, for one of a set of possible traffic ...

For warm-blooded species, the hazardous dose of a chemical (HD50) is an upcoming and important characteristic in the assessment of toxic chemicals. Generally, experimental information is available for a limited number of warm-blooded species only, which causes statistical uncertainty. Furthermore, when small datasets contain an unrepresentative sample of species, they can cause systematic uncer...

The modelling and simulation process in the automotive domain is transforming. Increasing system complexity variant diversity, especially new electric powertrain systems, lead to complex, modular simulations that depend on virtual vehicle development, testing approval. Consequently, emerging key requirements for validation involve a precise reliability quantification across large application do...

Abstract Statistical inference is the process of drawing conclusions about underlying population(s) using sample data to either confirm or falsify hypotheses. However, complexity real-life problems often makes statistical models inadequate, as information imprecise in many respects. To address this common problem, some papers have been published on modifications and extensions test concepts by ...