ABSTRACT

ABST R AC T Models of process dynamics often involve uncertain parameters, inputs and/or initial states. Even if probability distributions for the uncertainties are available, they too may be imprecise. An approach for rigorously and tightly bounding the effects of such uncertainty in process models is described here, and it is shown how this can be extended to determine rigorous bounds on the probabilities of achieving desired outcomes.