Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
A study of causal discovery with weak links and small samples
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Bayesian Artificial Intelligence, Second Edition
Bayesian Artificial Intelligence, Second Edition
Efficient Structure Learning of Bayesian Networks using Constraints
The Journal of Machine Learning Research
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Causal discovery with prior information
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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While a great variety of algorithms have been developed and applied to learning static Bayesian networks, the learning of dynamic networks has been relatively neglected. The causal discovery program CaMML has been enhanced with a highly flexible set of methods for taking advantage of prior expert knowledge in the learning process. Here we describe how these representations of prior knowledge can be used instead to turn CaMML into a promising tool for learning dynamic Bayesian networks.