Maximum entropy principle in non-ordered setting

Victor Maslov, Vladimir V'yugin

Research output: Contribution to journalConference articlepeer-review

Abstract

We consider the Maximum Entropy principle for non-ordered data in a non-probabilistic setting. The main goal of this paper is to deduce asymptotic relations for the frequencies of the energy levels in a non-ordered sequence ωN = [ω1,. . . , ωN] from the assumption of maximality of the Kolmogorov complexity K(ωN) given a constraint Σi=1N f(ωi) = NE, where E is a number and f is a numerical function.

Original languageEnglish
Pages (from-to)221-233
Number of pages13
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3244
DOIs
Publication statusPublished - 2004
Externally publishedYes
Event15th International Conference ALT 2004: Algorithmic Learning Theory - Padova, Italy
Duration: 2 Oct 20045 Oct 2004

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