Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Valuation-based systems: a framework for managing uncertainty in expert systems
Fuzzy logic for the management of uncertainty
Artificial Intelligence
Inner and outer approximation of belief structures using a hierarchical clustering approach
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Bayesian Networks Implementation of the Dempster Shafer Theory to Model Reliability Uncertainty
ARES '06 Proceedings of the First International Conference on Availability, Reliability and Security
International Journal of Approximate Reasoning
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Evidential reasoning with conditional belief functions
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Independence concepts in evidence theory
International Journal of Approximate Reasoning
Comparing evidential graphical models for imprecise reliability
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Compositional models and conditional independence in evidence theory
International Journal of Approximate Reasoning
Representing belief function knowledge with graphical models
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Causal belief networks: handling uncertain interventions
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
New propagation algorithm in dynamic directed evidential networks with conditional belief functions
IUKM'13 Proceedings of the 2013 international conference on Integrated Uncertainty in Knowledge Modelling and Decision Making
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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.