Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Computer-based probabilistic-network construction
Computer-based probabilistic-network construction
An algorithm for deciding if a set of observed independencies has a causal explanation
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Machine Learning
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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In this paper we have considered the problem of approximating an underlying distribution by one derived from a dependence polytree. This paper proposes a formal and systematic algorithm, which traverses the undirected tree obtained by the Chow method [2], and by using the independence tests it successfully orients the polytree. Our algorithm uses an application of the Depth First Search (DFS) strategy to multiple causal basins. The algorithm has been formally proven and rigorously tested for synthetic and real-life data.