Uncertainty in Artificial Intelligence
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Causal Independence for Knowledge Acquisition and Inference
David Heckerman
Abstract:
I introduce a temporal belief-network representation of causal independence that a knowledge engineer can use to elicit probabilistic models. Like the current, atemporal belief-network representation of causal independence, the new representation makes knowledge acquisition tractable. Unlike the atemproal representation, however, the temporal representation can simplify inference, and does not require the use of unobservable variables. The representation is less general than is the atemporal representation, but appears to be useful for many practical applications.
Keywords:
Pages: 122-127
PS Link:
PDF Link: /papers/93/p122-heckerman.pdf
BibTex:
@INPROCEEDINGS{Heckerman93,
AUTHOR = "David Heckerman ",
TITLE = "Causal Independence for Knowledge Acquisition and Inference",
BOOKTITLE = "Proceedings of the Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-93)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1993",
PAGES = "122--127"
}


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