  We present an exact approach to analyze and quantify the sensitivity of
higher moments of probabilistic loops with symbolic parameters, polynomial
arithmetic and potentially uncountable state spaces. Our approach integrates
methods from symbolic computation, probability theory, and static analysis in
order to automatically capture sensitivity information about probabilistic
loops. Sensitivity information allows us to formally establish how value
distributions of probabilistic loop variables influence the functional behavior
of loops, which can in particular be helpful when choosing values of loop
variables in order to ensure efficient/expected computations. Our work uses
algebraic techniques to model higher moments of loop variables via linear
recurrence equations and introduce the notion of sensitivity recurrences. We
show that sensitivity recurrences precisely model loop sensitivities, even in
cases where the moments of loop variables do not satisfy a system of linear
recurrences. As such, we enlarge the class of probabilistic loops for which
sensitivity analysis was so far feasible. We demonstrate the success of our
approach while analyzing the sensitivities of probabilistic loops.
