Statistical analysis with missing data
Statistical analysis with missing data
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Graph minors. XIII: the disjoint paths problem
Journal of Combinatorial Theory Series B
A tutorial on learning with Bayesian networks
Learning in graphical models
On the Desirability of Acyclic Database Schemes
Journal of the ACM (JACM)
A sufficiently fast algorithm for finding close to optimal clique trees
Artificial Intelligence
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Learning the Causal Structure of Overlapping Variable Sets
DS '02 Proceedings of the 5th International Conference on Discovery Science
Time and sample efficient discovery of Markov blankets and direct causal relations
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Graphs and Hypergraphs
Decomposition of search for v-structures in DAGs
Journal of Multivariate Analysis
Causal inference and causal explanation with background knowledge
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A Recursive Method for Structural Learning of Directed Acyclic Graphs
The Journal of Machine Learning Research
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In this paper, we propose that structural learning of a directed acyclic graph can be decomposed into problems related to its decomposed subgraphs. The decomposition of structural learning requires conditional independencies, but it does not require that separators are complete undirected subgraphs. Domain or prior knowledge of conditional independencies can be utilized to facilitate the decomposition of structural learning. By decomposition, search for d-separators in a large network is localized to small subnetworks. Thus both the efficiency of structural learning and the power of conditional independence tests can be improved.