Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assessments

Fabio Gagliardi Cozman; C. P. de Campos; J. S. Ide; Jose Carlos Ferreira da Rocha

Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assessments

Fabio Gagliardi Cozman, C. P. de Campos, J. S. Ide, Jose Carlos Ferreira da Rocha

Decision Support Systems

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

Abstract

This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and first-order constructs together with precise, imprecise, indeterminate and qualitative probabilistic assessments. The paper shows how this can be achieved through the theory of credal networks. New exact and approximate inference algorithms based on multilinear programming and iterated/loopy propagation of interval probabilities are presented; their superior performance, compared to existing ones, is shown empirically.

Original language	English
Title of host publication	Conference on Uncertainty in Artificial Intelligence (UAI)
Publisher	AUAI Press
Pages	104-111
Number of pages	8
Publication status	Published - 2004

Cite this

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title = "Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assessments",

abstract = "This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and first-order constructs together with precise, imprecise, indeterminate and qualitative probabilistic assessments. The paper shows how this can be achieved through the theory of credal networks. New exact and approximate inference algorithms based on multilinear programming and iterated/loopy propagation of interval probabilities are presented; their superior performance, compared to existing ones, is shown empirically.",

author = "Cozman, {Fabio Gagliardi} and {de Campos}, {C. P.} and Ide, {J. S.} and {da Rocha}, {Jose Carlos Ferreira}",

note = "(top 10%, plenary presentation, double-blind peer reviewed by >3 reviewers)",

year = "2004",

language = "English",

pages = "104--111",

booktitle = "Conference on Uncertainty in Artificial Intelligence (UAI)",

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Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assessments. / Cozman, Fabio Gagliardi; de Campos, C. P.; Ide, J. S. et al.
Conference on Uncertainty in Artificial Intelligence (UAI). AUAI Press, 2004. p. 104-111.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

TY - CHAP

T1 - Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assessments

AU - Cozman, Fabio Gagliardi

AU - de Campos, C. P.

AU - Ide, J. S.

AU - da Rocha, Jose Carlos Ferreira

N1 - (top 10%, plenary presentation, double-blind peer reviewed by >3 reviewers)

PY - 2004

Y1 - 2004

N2 - This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and first-order constructs together with precise, imprecise, indeterminate and qualitative probabilistic assessments. The paper shows how this can be achieved through the theory of credal networks. New exact and approximate inference algorithms based on multilinear programming and iterated/loopy propagation of interval probabilities are presented; their superior performance, compared to existing ones, is shown empirically.

AB - This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and first-order constructs together with precise, imprecise, indeterminate and qualitative probabilistic assessments. The paper shows how this can be achieved through the theory of credal networks. New exact and approximate inference algorithms based on multilinear programming and iterated/loopy propagation of interval probabilities are presented; their superior performance, compared to existing ones, is shown empirically.

M3 - Chapter

SP - 104

EP - 111

BT - Conference on Uncertainty in Artificial Intelligence (UAI)

PB - AUAI Press

ER -

Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assessments

Abstract

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