DocumentCode
1049329
Title
Approximation of the Two-Part MDL Code
Author
Adriaans, Pieter ; Vitányi, Paul M B
Author_Institution
Comput. Sci. Dept., Univ. of Amsterdam, Amsterdam
Volume
55
Issue
1
fYear
2009
Firstpage
444
Lastpage
457
Abstract
Approximation of the optimal two-part minimum description length (MDL) code for given data, through successive monotonically length-decreasing two-part MDL codes, has the following properties: (i) computation of each step may take arbitrarily long; (ii) we may not know when we reach the optimum, or whether we will reach the optimum at all; (iii) the sequence of models generated may not monotonically improve the goodness of fit; but (iv) the model associated with the optimum has (almost) the best goodness of fit. To express the practically interesting goodness of fit of individual models for individual data sets we have to rely on Kolmogorov complexity.
Keywords
codes; computational complexity; Kolmogorov complexity; data set; two-part MDL code; two-part minimum description length code; Computational modeling; Computer science; Entropy; Government; Information theory; Machine learning; Pattern analysis; Probability; Shape; Sorting; Approximation; Kolmogorov complexity; MDL code; minimum description length (MDL); model fitness; model selection; structure functions;
fLanguage
English
Journal_Title
Information Theory, IEEE Transactions on
Publisher
ieee
ISSN
0018-9448
Type
jour
DOI
10.1109/TIT.2008.2008152
Filename
4729775
Link To Document