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Average Case ϵ-Complexity in Computer Science: A Bayesian View

Joseph B. Kadane; Grzegorz W. Wasilkowski

Title:
Average Case ϵ-Complexity in Computer Science: A Bayesian View
Author(s):
Kadane, Joseph B.
Wasilkowski, Grzegorz W.
Date:
Type:
Technical reports
Department(s):
Computer Science
Persistent URL:
Series:
Columbia University Computer Science Technical Reports
Part Number:
CUCS-065-83
Publisher:
Department of Computer Science, Columbia University
Publisher Location:
New York
Abstract:
Relations between average case ϵ-complexity and Bayesian statistics are discussed. An algorithm corresponds to a decision function, and the choice of information to the choice of an experiment. Adaptive information in ϵ-complexity theory corresponds to the concept of sequential experiment. Some results are reported, giving ϵ-complexity and minimax-Bayesian interpretations for factor analysis. Results from ϵ-complexity are used to establish that the optimal sequential design is no better than optimal nonsequential design for that problem.
Subject(s):
Computer science
Applied mathematics
Item views
296
Metadata:
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Suggested Citation:
Joseph B. Kadane, Grzegorz W. Wasilkowski, , Average Case ϵ-Complexity in Computer Science: A Bayesian View, Columbia University Academic Commons, .

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