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Explaining inferences in Bayesian networks

✍ Scribed by Ghim-Eng Yap; Ah-Hwee Tan; Hwee-Hwa Pang


Publisher
Springer US
Year
2007
Tongue
English
Weight
658 KB
Volume
29
Category
Article
ISSN
0924-669X

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This paper reviews and formalizes algorithms for probabilistic inferences upon causal probabilistic networks (CPN), also known as Bayesian networks, and introduces Probanet-a development environment for CPNs. Information fusion in CPNs is realized through updating joint probabilities of the variable