Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Large-Sample Learning of Bayesian Networks is NP-Hard
The Journal of Machine Learning Research
Causality-Based Predicate Detection across Space and Time
IEEE Transactions on Computers
Thesis: incremental methods for Bayesian network structure learning
AI Communications
Learning Causal Bayesian Networks from Incomplete Observational Data and Interventions
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Active Learning for Causal Bayesian Network Structure with Non-symmetrical Entropy
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Causality: Models, Reasoning and Inference
Causality: Models, Reasoning and Inference
An incremental method for causal network construction
WAIM'10 Proceedings of the 11th international conference on Web-age information management
A Bayesian approach to learning causal networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Causal inference and causal explanation with background knowledge
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Sequential update of Bayesian network structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Using new data to refine a Bayesian network
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Theory refinement on Bayesian networks
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
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
Learning causal bayesian networks from observations and experiments: a decision theoretic approach
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
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This paper addresses modeling causal relationships over event streams where data are unbounded and hence incremental modeling is required. There is no existing work for incremental causal modeling over event streams. Our approach is based on Popper's three conditions which are generally accepted for inferring causality - temporal precedence of cause over effect, dependency between cause and effect, and elimination of plausible alternatives. We meet these conditions by proposing a novel incremental causal network construction algorithm. This algorithm infers causality by learning the temporal precedence relationships using our own new incremental temporal network construction algorithm and the dependency by adopting a state of the art incremental Bayesian network construction algorithm called the Incremental Hill-Climbing Monte Carlo. Moreover, we provide a mechanism to infer only strong causality, which provides a way to eliminate weak alternatives. This research benefits causal analysis over event streams by providing a novel two layered causal network without the need for prior knowledge. Experiments using synthetic and real datasets demonstrate the efficacy of the proposed algorithm.