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
Approximations of Causal Networks by Polytrees: an Empirical Study
IPMU'94 Selected papers from the 5th International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems, Advances in Intelligent Computing
ECSQAU Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Tractable Bayesian Learning of Tree Belief Networks
UAI '00 Proceedings of the 16th 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
The gaussian polytree EDA for global optimization
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Global optimization with the gaussian polytree EDA
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
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We consider the problem of approximating an underlying distribution by one derived from a dependence polytree. We propose a formal and systematic algorithm, which traverses the undirected tree obtained by the Chow method [IEEE Trans. Inform. Theory 14 (1968) 462], and which subsequently processes the latter using the knowledge of inter-node independence tests. By using the tree structure and these independence tests, our scheme successfully orients the polytree using 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.