• 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