ISAAC '02 Proceedings of the 13th International Symposium on Algorithms and Computation
Journal of Logic, Language and Information
Resource bounded symmetry of information revisited
Theoretical Computer Science - Mathematical foundations of computer science 2004
A method for parameter calibration and relevance estimation in evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Using Kolmogorov complexity for understanding some limitations on steganography
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 4
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
Non-approximability of the randomness deficiency function
CSR'06 Proceedings of the First international computer science conference on Theory and Applications
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In 1974 Kolmogorov proposed a non-probabilistic approach to statistics, an individual combinatorial relation between the data and its model. We vindicate, for the first time, the rightness of the original "structure function", proposed by Kolmogorov: minimizing the data-to-model code length (finding the ML estimator or MDL estimator), in a class of contemplated models of prescribed maximal (Kolmogorov) complexity, always results in a model of best fit (expressed as minimal randomness deficiency). We show that both the structure function and the minimum randomness deficiency function can assume all shapes over their full domain (improving an old result of L.A. Levin and both an old and a recent one of V.V. Vyugin). We determine the (un)computability properties of the various functions and "algorithmic sufficient statistic."