Perfect Sequences: A Contribution to Structuring Conditional Independence Models
نویسنده
چکیده
The representation of conditional independence models by perfect sequences provides an alternative to Bayesian networks and essential graphs. The paper discusses properties of perfect sequences that are relevant with respect to different structures of conditional independence models. Boundary variables (related to terminal nodes in a directed graph representation) are used to find the number of labeled and unlabeled models and to enumerate parts of the model space. Structuring principles are further applied to the evaluation of whole conditional independence models in learning models from data.
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