The authors investigate when Arrivals See Time Averages (ASTA) in a stochastic model; i.e., when the stationary distribution of an embedded sequence, obtained by observing a continuous-time stochastic process just prior to the points (arrivals) of an associated point process, coincides with the stationary distribution of the observed process. They also characterize the relation between the two distributions when ASTA does not hold. The authors introduce a Lack of Bias Assumption (LBA) which stipulates that, at any time, the conditional intensity of the point process, given the present state of the observed process, be independent of the state of the observed process. They show that LBA, without the Poisson assumption, is necessary and sufficient for ASTA in a stationary process framework. Consequently, LBA covers known examples of non-Poisson ASTA, such as certain flows in open Jackson queueing networks, as well as the familiar Poisson case (PASTA). The authors also establish results to cover the case in which the process is observed just after the points, e.g., when departures see time averages. Finally, they obtain a new proof of the Arrival Theorem for product-form queueing networks.
