Conditioning in Dempster-Shafer Theory: Prediction vs. Revision

We recall the existence of two methods for conditioning belief functions due to Dempster: one, known as Dempster conditioning, that applies Bayesian conditioning to the plausibility function and one that performs a sensitivity analysis on a conditional probability. We recall that while the first one is dedicated to revising a belief function, the other one is tailored to a prediction problem when the belief function is a statistical model. We question the use of Dempster conditioning for prediction tasks in Smets generalized Bayes theorem approach to the modeling of statistical evidence and propose a modified version of it, that is more informative than the other conditioning rule.