Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
A Causal Probabilistic Network for Optimal Treatment of Bacterial Infections
IEEE Transactions on Knowledge and Data Engineering
Global conditioning for probabilistic inference in belief networks
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Medical informatics: reasoning methods
Artificial Intelligence in Medicine
Machine learning for medical diagnosis: history, state of the art and perspective
Artificial Intelligence in Medicine
Diagnostic reasoning and medical decision-making with fuzzy influence diagrams
Computer Methods and Programs in Biomedicine
Diagnosis from bayesian networks with fuzzy parameters – a case in supply chains
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
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This work proposes a novel approach for solving abductive reasoning problems in Bayesian networks involving fuzzy parameters and extra constraints. The proposed method formulates abduction problems using nonlinear programming. To maximize the sum of the fuzzy membership functions subjected to various constraints, such as boundary, dependency and disjunctive conditions, unknown node belief propagation is completed. The model developed here can be built on any exact propagation methods, including clustering, joint tree decomposition, etc.