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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Independence for full conditional probabilities: Structure, factorization, non-uniqueness, and Bayesian networks

Full text
Author(s):
Cozman, Fabio G. [1]
Total Authors: 1
Affiliation:
[1] Univ Sao Paulo, BR-2231 Sao Paulo - Brazil
Total Affiliations: 1
Document type: Journal article
Source: INTERNATIONAL JOURNAL OF APPROXIMATE REASONING; v. 54, n. 9, p. 1261-1278, NOV 2013.
Web of Science Citations: 5
Abstract

This paper examines concepts of independence for full conditional probabilities; that is, for set-functions that encode conditional probabilities as primary objects, and that allow conditioning on events of probability zero. Full conditional probabilities have been used in economics, in philosophy, in statistics, in artificial intelligence. This paper characterizes the structure of full conditional probabilities under various concepts of independence; limitations of existing concepts are examined with respect to the theory of Bayesian networks. The concept of layer independence (factorization across layers) is introduced; this seems to be the first concept of independence for full conditional probabilities that satisfies the graphoid properties of Symmetry, Redundancy, Decomposition, Weak Union, and Contraction. A theory of Bayesian networks is proposed where full conditional probabilities are encoded using infinitesimals, with a brief discussion of hyperreal full conditional probabilities. (C) 2013 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 08/03995-5 - Logprob: probabilistic logic --- foundations and computational applications
Grantee:Marcelo Finger
Support Opportunities: Research Projects - Thematic Grants