Uncertainty in Artificial Intelligence
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Uncertain Reasoning Using Maximum Entropy Inference
Daniel Hunter
Abstract:
The use of maximum entropy inference in reasoning with uncertain information is commonly justified by an information-theoretic argument. This paper discusses a possible objection to this information-theoretic justification and shows how it can be met. I then compare maximum entropy inference with certain other currently popular methods for uncertain reasoning. In making such a comparison, one must distinguish between static and dynamic theories of degrees of belief: a static theory concerns the consistency conditions for degrees of belief at a given time; whereas a dynamic theory concerns how one's degrees of belief should change in the light of new information. It is argued that maximum entropy is a dynamic theory and that a complete theory of uncertain reasoning can be gotten by combining maximum entropy inference with probability theory, which is a static theory. This total theory, I argue, is much better grounded than are other theories of uncertain reasoning.
Keywords: Maximum Entropy Inference
Pages: 203-209
PS Link:
PDF Link: /papers/85/p203-hunter.pdf
BibTex:
@INPROCEEDINGS{Hunter85,
AUTHOR = "Daniel Hunter ",
TITLE = "Uncertain Reasoning Using Maximum Entropy Inference",
BOOKTITLE = "Uncertainty in Artificial Intelligence Annual Conference on Uncertainty in Artificial Intelligence (UAI-85)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1985",
PAGES = "203--209"
}


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