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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.)

Kuznetsov independence for interval-valued expectations and sets of probability distributions: Properties and algorithms

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Author(s):
Cozman, Fabio G. [1] ; de Campos, Cassio Polpo [2]
Total Authors: 2
Affiliation:
[1] Univ Sao Paulo, Sao Paulo - Brazil
[2] Ist Dalle Molle Studi Intelligenza Artificiale ID, CH-6928 Manno - Switzerland
Total Affiliations: 2
Document type: Journal article
Source: INTERNATIONAL JOURNAL OF APPROXIMATE REASONING; v. 55, n. 2, p. 666-682, JAN 2014.
Web of Science Citations: 7
Abstract

Kuznetsov independence of variables X and Y means that, for any pair of bounded functions f(X) and g(Y), E{[}f(X)g(Y)] = E{[}f(X)] boxed times E{[}g(Y)], where IE{[}.] denotes interval-valued expectation and boxed times denotes interval multiplication. We present properties of Kuznetsov independence for several variables, and connect it with other concepts of independence in the literature; in particular we show that strong extensions are always included in sets of probability distributions whose lower and upper expectations satisfy Kuznetsov independence. We introduce an algorithm that computes lower expectations subject to judgments of Kuznetsov independence by mixing column generation techniques with nonlinear programming. Finally, we define a concept of conditional Kuznetsov independence, and study its graphoid properties. (C) 2013 Elsevier Inc. All rights reserved. (AU)