Using causal reasoning to validate stochastic models

An important problem of validating stochastic models is addressed. Validating stochastic models is necessary for modeling high-performance and highly dependable computers accurately. This paper develops a model validation methodology using causal reasoning. More specifically, this technique uses the structural and behavioral knowledge derived from the system specification and a causal reasoning mechanism for validation purposes. The scope of this research is limited to the conceptual validation of Markov models. Conceptual validation, as opposed to empirical validation, does not require the use of data. The validation process primarily involves generating a reference object, translating the given model into a common format, and comparing the two objects to identify holes and inconsistencies. Event trees are used as the common format. The effectiveness of this methodology is tested by validating models of five example systems. For testing purposes, errors are introduced into the models of these systems.<>