  In this article we construct a theoretical and computational process for
assessing Input Probability Sensitivity Analysis (IPSA) using a Graphics
Processing Unit (GPU) enabled technique called Vectorized Uncertainty
Propagation (VUP). VUP propagates probability distributions through a
parametric computational model in a way that's computational time complexity
grows sublinearly in the number of distinct propagated input probability
distributions. VUP can therefore be used to efficiently implement IPSA, which
estimates a model's probabilistic sensitivity to measurement and parametric
uncertainty over each relevant measurement location. Theory and simulation
illustrate the effectiveness of these methods.
