Publication
Title
From inductive inference to the fundamental equation of measurement
Author
Abstract
By considering inductive inference of the viewpoint of a gradual inclusion of information, instead of forecasting a given sequence, it will be shown that conditional algorithmic complexity decreases during learning. Based on a theorem of Levin, conditional algorithmic complexity and mutual algorithmic complexity are shown to be approximated by conditional entropy and mutual information, respectively. Furthermore, physical randomness and physical complexity are shown to be given by conditional algorithmic complexity and mutual algorithmic complexity, hence sum up to algorithmic complexity. A relation between computation and measurement will be suggested.
Language
English
Source (book)
2nd International Conference on Complex Systems, Oct; 25-30, 1998, New England Complex Syst nst., Nashua, N.H.
Publication
Cambridge : Perseus, 2000
ISBN
0-7382-0049-2
Volume/pages
(2000), p. 115-122
ISI
000086160100012
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 01.03.2012
Last edited 06.05.2017
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