Accession Number:

ADA250538

Title:

Bayesian and Non-Bayesian Evidential Updating

Descriptive Note:

Corporate Author:

ROCHESTER UNIV NY DEPT OF PHILOSOPHY

Personal Author(s):

Report Date:

1985-01-01

Pagination or Media Count:

43.0

Abstract:

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 Shafers 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 DempsterShafer updating on uncertain evidence are included in and may be properly included in the intervals that result form Bayesian updating on uncertain evidence.

Subject Categories:

  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE