Uncertainty in Artificial Intelligence
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Justifying the Principle of Interval Constraints
Richard Neapolitan, James Kenevan
Abstract:
When knowledge is obtained from a database, it is only possible to deduce confidence intervals for probability values. With confidence intervals replacing point values, the results in the set covering model include interval constraints for the probabilities of mutually exclusive and exhaustive explanations. The Principle of Interval Constraints ranks these explanations by determining the expected values of the probabilities based on distributions determined from the interval, constraints. This principle was developed using the Classical Approach to probability. This paper justifies the Principle of Interval Constraints with a more rigorous statement of the Classical Approach and by defending the concept of probabilities of probabilities.
Keywords:
Pages: 266-274
PS Link:
PDF Link: /papers/88/p266-neapolitan.pdf
BibTex:
@INPROCEEDINGS{Neapolitan88,
AUTHOR = "Richard Neapolitan and James Kenevan",
TITLE = "Justifying the Principle of Interval Constraints",
BOOKTITLE = "Proceedings of the Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-88)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1988",
PAGES = "266--274"
}


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