Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning in graphical models
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
Causal networks: semantics and expressiveness
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Using the structure of d-connecting paths as a qualitative measure of the strength of dependence
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Large-sample learning of bayesian networks is NP-hard
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Minimal separators in dependency structures: Properties and identification
Cybernetics and Systems Analysis
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Problems of reconstruction of structures of probabilistic dependence models in the class of directed (oriented) acyclic graphs (DAGs) and mono-flow graphs are considered. (Mono-flow graphs form a subclass of DAGs in which the cycles with one collider are prohibited.) The technique of induced (provoked) dependences is investigated and its application to the identification of structures of models is shown. The algorithm "Collifinder-M" is developed that identifies all collider variables (i.e., solves an intermediate problem of reconstruction of the structure of a mono-flow model). It is shown that a generalization of the technique of induced dependences makes it possible to strengthen well-known rules of identification of orientation of edges in a DAG model.