Axioms for Probability and Belief-Function Propagation

Prakash P. Shenoy, Glenn Shafer

Research output: Chapter in Book/Report/Conference proceedingChapter

166 Scopus citations


In this paper, we describe an abstract framework and axioms under which exact local computation of marginals is possible. The primitive objects of the framework are variables and valuations. The primitive operators of the framework are combination and marginalization. These operate on valuations. We state three axioms for these operators and we derive the possibility of local computation from the axioms. Next, we describe a propagation scheme for computing marginals of a valuation when we have a factorization of the valuation on a hypertree. Finally we show how the problem of computing marginals of joint probability distributions and joint belief functions fits the general framework.

Original languageEnglish (US)
Title of host publicationMachine Intelligence and Pattern Recognition
Number of pages30
StatePublished - Jan 1 1990
Externally publishedYes

Publication series

NameMachine Intelligence and Pattern Recognition
ISSN (Print)0923-0459

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


Dive into the research topics of 'Axioms for Probability and Belief-Function Propagation'. Together they form a unique fingerprint.

Cite this