Sensitivity forecasts inform the design of experiments and the direction of
theoretical efforts. We argue that to arrive at representative results Bayesian
forecasts should marginalize their conclusions over uncertain parameters and
noise realizations rather than picking fiducial values. However, this is
computationally infeasible with current methods. We thus propose a novel
simulation-based forecasting methodology, which we find to be capable of
providing expedient rigorous forecasts without relying on restrictive
assumptions.Comment: 5 pages + references, 1 figure. Submitted to PR