Introduces separable and essentially separable graphs as a broad class for mixed graphical models, provides multiple characterizations of the graphs and their separation equivalence, and develops an identification algorithm for equivalence classes.
Strong Completeness and Faithfulness in Bayesian Networks
1 Pith paper cite this work. Polarity classification is still indexing.
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Pith paper citing it
abstract
A completeness result for d-separation applied to discrete Bayesian networks is presented and it is shown that in a strong measure-theoretic sense almost all discrete distributions for a given network structure are faithful; i.e. the independence facts true of the distribution are all and only those entailed by the network structure.
fields
stat.ML 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
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Characterizing and Identifying Separable Graphical Models
Introduces separable and essentially separable graphs as a broad class for mixed graphical models, provides multiple characterizations of the graphs and their separation equivalence, and develops an identification algorithm for equivalence classes.