  We provide a novel method for sensitivity analysis of parametric robust
Markov chains. These models incorporate parameters and sets of probability
distributions to alleviate the often unrealistic assumption that precise
probabilities are available. We measure sensitivity in terms of partial
derivatives with respect to the uncertain transition probabilities regarding
measures such as the expected reward. As our main contribution, we present an
efficient method to compute these partial derivatives. To scale our approach to
models with thousands of parameters, we present an extension of this method
that selects the subset of $k$ parameters with the highest partial derivative.
Our methods are based on linear programming and differentiating these programs
around a given value for the parameters. The experiments show the applicability
of our approach on models with over a million states and thousands of
parameters. Moreover, we embed the results within an iterative learning scheme
that profits from having access to a dedicated sensitivity analysis.
