Analogical and inductive reasoning
Analogical and inductive reasoning
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Confirmation-guided discovery of first-order rules with tertius
Machine Learning
Proceedings of the 1st ACM SIGACT-SIGMOD symposium on Principles of database systems
PODS '82 Proceedings of the 1st ACM SIGACT-SIGMOD symposium on Principles of database systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Discovery of causality and acausality from temporal sequential data
Discovery of causality and acausality from temporal sequential data
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Discovering temporal/causal rules: a comparison of methods
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Distinguishing causal and acausal temporal relations
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
The TIMERS II algorithm for the discovery of causality
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A Novel Scalable and Data Efficient Feature Subset Selection Algorithm
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
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In current constraint-based (Pearl-style) systems for discovering Bayesian networks, inputs with deterministic relations are prohibited. This restricts the applicability of these systems. In this paper, we formalize a sufficient condition under which Bayesian networks can be recovered even with deterministic relations. The sufficient condition leads to an improvement to Pearl's IC algorithm; other constraint-based algorithms can be similarly improved. The new algorithm, assuming the sufficient condition proposed, is able to recover Bayesian networks with deterministic relations, and moreover suffers no loss of performance when applied to nondeterministic Bayesian networks.