Estimating the Mass of the Higgs Particle Using Dempster - Shafer Analysis

We present a Dempster-Shafer (DS) approach to finding confidence bounds on the mass of the Higgs boson. Dempster-Shafer is a statistical framework that generalizes Bayesian statistics. DS calculus augments traditional probability by allowing mass to be distributed over power sets of the event space. This eliminates the Bayesian dependence on prior distributions while allowing the incorporation of prior information when it is available. We use the Poisson DempsterShafer model (DSM) to derive a posterior DSM for the Banff threePoisson model, from which we make inferences about the unknown mass of the Higgs particle. The results compare favorably with other approaches, demonstrating the utility of the approach. We argue that the reduced dependence on priors afforded by the Dempster-Shafer framework is both practically and theoretically desirable.