Journal of Complexity
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Connectionist learning procedures
Artificial Intelligence
Graphoids: a qualitative framework for probabilistic inference
Graphoids: a qualitative framework for probabilistic inference
Graph Algorithms
Causal networks: semantics and expressiveness
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
On testing whether an embedded Bayesian network represents a probability model
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Using causal information and local measures to learn Bayesian networks
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
A construction of Bayesian networks from databases based on an MDL principle
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Theory refinement on Bayesian networks
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
A Bayesian method for constructing Bayesian belief networks from databases
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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In constructing probabilistic networks from human judgments, we use causal relationships to convey useful patterns of dependencies. The converse task, that of inferring causal relationships from patterns of dependencies, is far less understood. This paper establishes conditions under which the directionality of some interactions can be determined from non-temporal probabilistic information -- an essential prerequisite for attributing a causal interpretation to these interactions. An efficient algorithm is developed that, given data generated by an undisclosed causal polytree, recovers the structure of the underlying polytree, as well as the directionality of all its identifiable links.