Fully Bayesian Forecasts with Evidence Networks

Abstract

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