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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

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

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Autor(es):
Cozman, Fabio G. [1]
Número total de Autores: 1
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, BR-2231 Sao Paulo - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo Científico
Fonte: INTERNATIONAL JOURNAL OF APPROXIMATE REASONING; v. 54, n. 9, p. 1261-1278, NOV 2013.
Citações Web of Science: 5
Resumo

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)

Processo FAPESP: 08/03995-5 - LOGPROB: lógica probabilística - fundamentos e aplicações computacionais
Beneficiário:Marcelo Finger
Linha de fomento: Auxílio à Pesquisa - Temático