Uncertainty in Artificial Intelligence
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Belief Revision in Probability Theory
Pei Wang
Abstract:
In a probability-based reasoning system, Bayes' theorem and its variations are often used to revise the system's beliefs. However, if the explicit conditions and the implicit conditions of probability assignments `me properly distinguished, it follows that Bayes' theorem is not a generally applicable revision rule. Upon properly distinguishing belief revision from belief updating, we see that Jeffrey's rule and its variations are not revision rules, either. Without these distinctions, the limitation of the Bayesian approach is often ignored or underestimated. Revision, in its general form, cannot be done in the Bayesian approach, because a probability distribution function alone does not contain the information needed by the operation.
Keywords:
Pages: 519-526
PS Link:
PDF Link: /papers/93/p519-wang.pdf
BibTex:
@INPROCEEDINGS{Wang93,
AUTHOR = "Pei Wang ",
TITLE = "Belief Revision in Probability Theory",
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 = "519--526"
}


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