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We provide a novel method for sensitivity analysis of parametric robustMarkov chains. These models incorporate parameters and sets of probabilitydistributions to alleviate the often unrealistic assumption that preciseprobabilities are available. We measure sensitivity in terms of partialderivatives with respect to the uncertain transition probabilities regardingmeasures such as the expected reward. As our main contribution, we present anefficient method to compute these partial derivatives. To scale our approach tomodels with thousands of parameters, we present an extension of this methodthat selects the subset of $k$ parameters with the highest partial derivative.Our methods are based on linear programming and differentiating these programsaround a given value for the parameters. The experiments show the applicabilityof our approach on models with over a million states and thousands ofparameters. Moreover, we embed the results within an iterative learning schemethat profits from having access to a dedicated sensitivity analysis.

Efficient Sensitivity Analysis for Parametric Robust Markov Chains