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Information in metric space

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Bibliographic Details
Title: Information in metric space
Authors: Pappalardo, M.1 mpappalardo@unisa.it
Source: Journal of Materials Processing Technology. Dec2004, Vol. 157-158, p228-231. 4p.
Subjects: Numerical analysis, Probability theory, Data analysis, Approximation theory
Abstract: Abstract: The idea of information, in the classic theories 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 the introduction of a Theory of Information for events not connected to the probability therefore for non-repetitive events. On the basis of so called Laplace''s 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. [Copyright &y& Elsevier]
Copyright of Journal of Materials Processing Technology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 15838200
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Abstract: The idea of information, in the classic theories 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 the introduction of a Theory of Information for events not connected to the probability therefore for non-repetitive events. On the basis of so called Laplace''s 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. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Materials Processing Technology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.jmatprotec.2004.09.034
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      – Code: eng
        Text: English
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        PageCount: 4
        StartPage: 228
    Subjects:
      – SubjectFull: Numerical analysis
        Type: general
      – SubjectFull: Probability theory
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Approximation theory
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      – TitleFull: Information in metric space
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              Text: Dec2004
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              Y: 2004
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