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Information in metric space
by
Yasaman Izadparast
student M.Sc. statistic Islamic Azad University North Tehran Branch
Coauthors: E.Pasha Department of Mathematics Teacher Training University Tehran-Iran E-mail:Pasha@saba.tmu.ac.ir
Hamidreza Mostafaei
Depatrment of statistic Islamic Azad University North Tehran Branch
E-mail:hamidmostafaei@yahoo.com
The idea of information in the classic of Fisher and Wiener-Shannon, is a measure only of probabilistic and repetitiveness events.
The idea of information is broader than the probability. The Wiener-Shannon’s axioms are extended to the non –probabilistic and repetitiveness events. it is possible introduction of a Theory of Information for events not connected to the probability therefore for non- repetitive events.
On the basis of so called Lap laces principle of insufficient knowledge, the MaxInf principle is defined for choose solutions in absence of knowledge In this paper the value of information, as a measure of equality of data among a set of values, is applied in numeric analysis as method for approximation of data, as an example of application.
Key words: MaxEnt; Probability; Entropy; Classification; Approximation
Date received: February 10, 2007
Copyright © 2007 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # cata-69.