ABSTRACT
Stationary statistical properties of a system, can easily be estimated by running a single discrete event simulation on the system over a sufficiently long time horizon, or by working directly on the stationary availabilities. Sometimes, however, one needs to estimate how the statistical properties of the system evolve over time. In such cases it is necessary to run many simulations to obtain stable results. Moreover, one must store muchmore information fromeach simulation. A crude approach to this problem is to sample the system state
at fixed intervals of time, and then use the mean values of the states at these points as estimates of the corresponding statistical properties. Using a sufficiently high sampling rate, i.e., short intervals between sampling points, a satisfactory estimate of the full curve can be obtained. Still, all information about the process between the samplingpoints is thrownaway. Thus, we propose an alternative sampling procedure where we utilize process data between the sampling points as well.
