Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Semigraphoids and structures of probabilistic conditional independence
Annals of Mathematics and Artificial Intelligence
Probabilistic Conditional Independence Structures: With 42 Illustrations (Information Science and Statistics)
Racing algorithms for conditional independence inference
International Journal of Approximate Reasoning
Conditional independence structure and its closure: Inferential rules and algorithms
International Journal of Approximate Reasoning
Acyclic Directed Graphs to Represent Conditional Independence Models
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Acyclic directed graphs representing independence models
International Journal of Approximate Reasoning
Causal inference and causal explanation with background knowledge
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
On the implication problem for probabilistic conditional independency
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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The representation problem of independence models is studied by focusing on acyclic directed graph (DAG). We present the algorithm PC* in order to look for a perfect map. However, when a perfect map does not exist, so that PC* fails, it is interesting to find a minimal I--map, which represents as many triples as possible in J*. Therefore we describe an algorithm which finds such a map by means of a backtracking procedure.