Robust power series algorithm for epistemic uncertainty propagation in Markov chain models

In this article, we develop a new methodology for integrating epistemic uncertainties into the computation of performance measures of Markov chain models. We developed a power series algorithm that allows for combining perturbation analysis and uncertainty analysis in a joint framework. We characterize statistically several performance measures, given that distribution of the model parameter expressing the uncertainty about the exact parameter value is known. The technical part of the article provides convergence result, bounds for the remainder term of the power series, and bounds for the validity region of the approximation. In the algorithmic part of the article, an efficient implementation of the power series algorithm for propagating epistemic uncertainty in queueing models with breakdowns and repairs is discussed. Several numerical examples are presented to illustrate the performance of the proposed algorithm and are compared with the corresponding Monte Carlo simulations ones