BAYESIAN AND NON-BAYESIAN EVIDENTIAL UPDATING

Four main results are arrived at in this paper (1) Closed convex sets of classical probability functions provide a representation of belief that includes the representations provided by Shafer probability mass functions as a special case. (2) The impact of 'uncertain evidence' can be (formally) represented by Dempster conditioning, in Shafer's framework. (3) The impact of 'uncertain evidence' can be (formally) represented in the framework of convex sets of classical probabilities by classical conditionalization. (4) The probability intervals that result from Dempster-Shafer updating on uncertain evidence are included in (and may be properly included in) the intervals that result from Bayesian updating on uncertain evidence.