Surrogate Models for Markov Reward Models with Uncertain Parameters

Markov Reward Models are widely used for performance or dependability analysis. Usually parameters of these models are assumed to be exactly known, results can then be computed numerically from the underlying Markov Reward Process. However, most times model parameters are estimated and are thus subject to uncertainty such that analysis becomes challenging and is usually done by analyzing the model for a large sample of possible input parameter settings which requires a huge effort if several parameters are uncertain. An alternative to this brute force approach is the computation of a simpler surrogate model from a smaller set of samples and apply the surrogate as a substitute for the Markov Reward Model. This paper presents several surrogate models, shows how they can be applied for Markov Reward Models and compares their quality by means of three different examples.