HIGHLIGHTS
- who: Variables, and Alessandro, Magrini from the (UNIVERSITY) have published the paper: E ffi cient Decomposition of Bayesian Networks With Non-graded Variables, in the Journal: (JOURNAL)
- what: The authors propose the causal independence decomposition which includes the Noisy-MAX and two generalizations suited to double-graded and multi-valued nominal While the general definition of BN implicitly assumes the presence of all the possible causal interactions the proposal is based on causal independence and causal interaction is a feature that can be added upon need. The authors focus on the number of free parameters defining . . .
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