Uncertainty in Artificial Intelligence
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Learning Link-Probabilities in Causal Trees
Igor Roizer, Judea Pearl
Abstract:
A learning algorithm is presented which given the structure of a causal tree, will estimate its link probabilities by sequential measurements on the leaves only. Internal nodes of the tree represent conceptual (hidden) variables inaccessible to observation. The method described is incremental, local, efficient, and remains robust to measurement imprecisions.
Keywords:
Pages: 211-214
PS Link:
PDF Link: /papers/86/p211-roizer.pdf
BibTex:
@INPROCEEDINGS{Roizer86,
AUTHOR = "Igor Roizer and Judea Pearl",
TITLE = "Learning Link-Probabilities in Causal Trees",
BOOKTITLE = "Proceedings of the Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-86)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1986",
PAGES = "211--214"
}


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