Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Resolving misunderstandings about belief functions
International Journal of Approximate Reasoning - Special issue: The belief functions revisited: questions and answers
Graphoid properties of qualitative possibilistic independence relations
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
International Journal of Approximate Reasoning
Inference in directed evidential networks based on the transferable belief model
International Journal of Approximate Reasoning
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks
Comparing evidential graphical models for imprecise reliability
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Valuation networks and conditional independence
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Causal belief networks: handling uncertain interventions
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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Belief function theory is an appropriate framework to model different forms of knowledge including probabilistic knowledge. One simple and efficient way to reason under uncertainty, is the use of compact graphical models, namely directed acyclic graphs. Therefore naturally, a question crosses the mind: If we deal with Bayesian belief knowledge does the network collapse into a Bayesian network? This paper attempts to answer this question by analyzing different forms of belief function networks defined with conditional beliefs defined either with a unique conditional distribution for all parents or a single conditional distribution for each single parent. We also propose a new method for the deconditionalization process to compute the joint distribution.