Uncertainty in Artificial Intelligence
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Combination of Evidence Using the Principle of Minimum Information Gain
Michael Wong, P. Lingras
One of the most important aspects in any treatment of uncertain information is the rule of combination for updating the degrees of uncertainty. The theory of belief functions uses the Dempster rule to combine two belief functions defined by independent bodies of evidence. However, with limited dependency information about the accumulated belief the Dempster rule may lead to unsatisfactory results. The present study suggests a method to determine the accumulated belief based on the premise that the information gain from the combination process should be minimum. This method provides a mechanism that is equivalent to the Bayes rule when all the conditional probabilities are available and to the Dempster rule when the normalization constant is equal to one. The proposed principle of minimum information gain is shown to be equivalent to the maximum entropy formalism, a special case of the principle of minimum cross-entropy. The application of this principle results in a monotonic increase in belief with accumulation of consistent evidence. The suggested approach may provide a more reasonable criterion for identifying conflicts among various bodies of evidence.
Pages: 450-459
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PDF Link: /papers/90/p450-wong.pdf
AUTHOR = "Michael Wong and P. Lingras",
TITLE = "Combination of Evidence Using the Principle of Minimum Information Gain",
BOOKTITLE = "Proceedings of the Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-90)",
ADDRESS = "Corvallis, Oregon",
YEAR = "1990",
PAGES = "450--459"

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