Evidence-invariant Sensitivity Bounds
  The sensitivities revealed by a sensitivity analysis of a probabilistic
network typically depend on the entered evidence. For a real-life network
therefore, the analysis is performed a number of times, with different
evidence. Although efficient algorithms for sensitivity analysis exist, a
complete analysis is often infeasible because of the large range of possible
combinations of observations. In this paper we present a method for studying
sensitivities that are invariant to the evidence entered. Our method builds
upon the idea of establishing bounds between which a parameter can be varied
without ever inducing a change in the most likely value of a variable of
interest.
