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Abstract: We present a hybrid constraintbased /Bayesian algorithm for learning causal networks in the presence of sparse data. The algorithm searches the space of equivalence classes of models (essential graphs) using a heuristic based on conventional constraintbased techniques. Each essential graph is then converted into a directed acyclic graph and scored using a Bayesian scoring metric. (Update)
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BibTeX entry: (Update)
Dash, D. and Druzdzel, M., A hybrid anytime algorithm for the construction of causal models from sparse data, Proceedings of the fifteenth international conference on uncertainty in artificial intelligence, 1999. http://citeseer.ist.psu.edu/dash99hybrid.html More
@inproceedings{ dashhybrid,
author = "Denver Dash and Marek J. Druzdzel",
title = "A Hybrid Anytime Algorithm for the Construction of Causal Models From Sparse Data",
pages = "142--149",
url = "citeseer.ist.psu.edu/dash99hybrid.html" }
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