Inference in directed evidential networks based on the transferable belief model

被引:49
作者
Ben Yaghlane, Boutbeina [1 ]
Mellouli, Khaled [1 ]
机构
[1] Univ Tunis, LARODEC, IHEC Carthage Presidence, Tunis 2016, Tunisia
关键词
belief functions; conditional belief functions; directed evidential networks; binary join tree;
D O I
10.1016/j.ijar.2008.01.002
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Inference algorithms in directed evidential networks (DEVN) obtain their efficiency by making use of the represented independencies between variables in the model. This can be done using the disjunctive rule of combination (DRC) and the generalized Bayesian theorem (GBT), both proposed by Smets [Ph. Smets, Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem, International Journal of Approximate Reasoning 9 (1993) 1-35]. These rules make possible the use of conditional belief functions for reasoning in directed evidential networks, avoiding the computations of joint belief function on the product space. In this paper, new algorithms based on these two rules are proposed for the propagation of belief functions in singly and multiply directed evidential networks. (C) 2008 Elsevier Inc. All rights reserved.
引用
收藏
页码:399 / 418
页数:20
相关论文
共 32 条
[1]  
[Anonymous], 1988, PROBABILISTIC REASON, DOI DOI 10.1016/C2009-0-27609-4
[2]  
[Anonymous], IJCAI
[3]  
[Anonymous], 1992, fuzzy Logic for the Management of Uncertainty
[4]  
Ben Yaghlane B, 2003, LECT NOTES ARTIF INT, V2711, P291
[5]   Belief function independence: I. The marginal case [J].
Ben Yaghlane, B ;
Smets, P ;
Mellouli, K .
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2002, 29 (01) :47-70
[6]  
BENYAGHLANE B, 2006, P INT C INF PROC MAN, P1451
[7]  
BENYAGHLANE B, 2002, INT J APPROX REASON, V31, P31
[8]   AN AXIOMATIC FRAMEWORK FOR PROPAGATING UNCERTAINTY IN DIRECTED ACYCLIC NETWORKS [J].
CANO, J ;
DELGADO, M ;
MORAL, S .
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 1993, 8 (04) :253-280
[9]   On the plausibility transformation method for translating belief function models to probability models [J].
Cobb, BR ;
Shenoy, PP .
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2006, 41 (03) :314-330
[10]   Independency relationships and learning algorithms for singly connected networks [J].
De Campos, LM .
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, 1998, 10 (04) :511-549