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; 2530, 1998, New England Complex Syst nst., Nashua, N.H.  
Publication 



Cambridge : Perseus, 2000
 
ISBN 



0738200492
 
Volume/pages 



(2000), p. 115122
 
ISI 



000086160100012
 
