Uncertainty in Artificial Intelligence
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Independence with Lower and Upper Probabilities
Lonnie Chrisman
It is shown that the ability of the interval probability representation to capture epistemological independence is severely limited. Two events are epistemologically independent if knowledge of the first event does not alter belief (i.e., probability bounds) about the second. However, independence in this form can only exist in a 2-monotone probability function in degenerate cases~--- i.e., if the prior bounds are either point probabilities or entirely vacuous. Additional limitations are characterized for other classes of lower probabilities as well. It is argued that these phenomena are simply a matter of interpretation. They appear to be limitations when one interprets probability bounds as a measure of epistemological indeterminacy (i.e., uncertainty arising from a lack of knowledge), but are exactly as one would expect when probability intervals are interpreted as representations of ontological indeterminacy (indeterminacy introduced by structural approximations). The ontological interpretation is introduced and discussed.
Keywords: Lower probability, interval probabilities, independence, abstraction, ontological in
Pages: 169-177
PS Link: http://www.cs.cmu.edu/~chrisman/mypapers/uai96b.html
PDF Link: /papers/96/p169-chrisman.pdf
AUTHOR = "Lonnie Chrisman ",
TITLE = "Independence with Lower and Upper Probabilities",
BOOKTITLE = "Proceedings of the Twelfth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-96)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1996",
PAGES = "169--177"

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