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doi:10.1016/0888-613X(92)90002-H    
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Copyright © 1992 Published by Elsevier Science Inc.

A computational normative theory of scientific evidence

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David B. SherCorresponding Author Contact Information

Computer Science Department, State University of New York at, Buffalo, New York, USA


Received 1 July 1990; 
accepted 15 September 1991. ;
Available online 20 May 2003.

Abstract

A scientific reasoning system makes decisions using objective evidence in the form of independent experimental trials, propositional axioms, and constraints on the probabilities of events. I propose a collection of algorithms that derive probability intervals and estimate conditional probabilities from objective evidence in those forms. This reasoning system can manage uncertainty about data and rules in a rule-based expert system. I expect that the system will be particularly applicable to diagnosis and analysis in domains with a wealth of experimental evidence such as medicine. The algorithms currently apply to systems with arbitrary amounts of experimental evidence but with less than 20 variables. I discuss limitations of this solution and propose future directions for this research. This work can be considered a generalization of Nilsson's “probabilistic logic” to intervals and experimental observations.

Author Keywords: interval probability; evidence combination; experimental evidence; probabilistic logic; statistical inference

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Corresponding Author Contact InformationCorresponding author. Address correspondence to David Sher, Computer Science Department, SUNY Buffalo, Buffalo, NY 14260.


 
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