|Title||Keynote Paper: Parametric Uncertainty Propagation through Dependability Models|
|Publication Type||Conference Paper|
|Year of Publication||2019|
|Authors||H Okamura, T Dohi, and K Trivedi|
|Conference Name||Proceedings 8th Latin American Symposium on Dependable Computing, Ladc 2018|
© 2018 IEEE. The uncertainty propagation is to investigate the effect of errors in model input parameters on the system output measure in probability models. In this paper, we present a moment-based approach of the uncertainty propagation of model input parameters. The presented approach requires only the fist two moments of model parameters, and has an advantage in terms of computation over the closed-form, numerical and sampling-based approaches for uncertainty propagation. The paper presents the properties of moment-based approach by comparing the existing Bayes estimation for the uncertainty propagation in a simple reliability model. An availability model of a server with virtual machines is used to illustrate the applicability of our method in practical problems.