Meaningful information

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2518 LNCS, Issue , 2002, Pages 588-599

  Vitányi, Paul ^a  

^a CWI   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMIC PROPERTIES; ALGORITHMIC STATISTICS; HALTING PROBLEMS; KOLMOGOROV COMPLEXITY; NON-STOCHASTIC; PROBABILITY MASS FUNCTION; RESTRICTED MODELS; SUFFICIENT STATISTICS;

PROBABILITY DISTRIBUTIONS; RECURSIVE FUNCTIONS; SET THEORY; STATISTICS; ALGORITHMS;

ALGORITHMS; PROBABILITY DISTRIBUTIONS;

EID: 44349085881     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-36136-7_51     Document Type: Conference Paper

References (20)

1. A.R. Barron, J. Rissanen, and B. Yu, The minimum description length principle in coding and modeling, IEEE Trans. Inform. Theory, IT-44:6(1998), 2743-2760. (Pubitemid 128741906)
2. T.M. Cover, Kolmogorov complexity, data compression, and inference, pp. 23-33 in: The Impact of Processing Techniques on Communications, J.K. Skwirzynski, Ed., Martinus Nijhoff Publishers, 1985.
3. T.M. Cover and J.A. Thomas, Elements of Information Theory, Wiley, New York, 1991.
4. R. A. Fisher, On the mathematical foundations of theoretical statistics, Philosophical Transactions of the Royal Society of London, Ser. A, 222(1922), 309-368.
5. P. Gacs, On the symmetry of algorithmic information, Soviet Math. Dokl, 15 (1974) 1477-1480. Correction: ibid., 15 (1974) 1480.
6. P. Gacs, J. Tromp, and P. Vitanyi, Algorithmic statistics, IEEE Trans. Inform. Theory, 47:6(2001), 2443-2463. (Pubitemid 32949894)
7. Q. Gao, M. Li and P.M.B. Vitanyi, Applying MDL to learn best model granularity, Artificial Intelligence, 121(2000), 1-29.
8. M. Gell-Mann, The Quark and the Jaguar, W. H. Freeman and Company, New York, 1994.
9. A.N. Kolmogorov, Three approaches to the quantitative definition of information, Problems Inform. Transmission 1:1 (1965) 1-7.
10. A.N. Kolmogorov, On logical foundations of probability theory, Pp. 1-5 in: Probability Theory and Mathematical Statistics, Lect. Notes Math., Vol. 1021, K. Itô and Yu. V. Prokhorov, Eds., Springer-Verlag, Heidelberg, 1983.
11. A.N. Kolmogorov and V.A. Uspensky, Algorithms and Randomness, SIAM Theory Probab. Appl., 32:3(1988), 389-412.
12. M. Koppel, Complexity, depth, and sophistication, Complex Systems, 1(1987), 1087-1091
13. M. Koppel, Structure, The Universal Turing Machine: A Half-Century Survey, R. Herken (Ed.), Oxford Univ. Press, 1988, pp. 435-452.
14. M. Li and P. Vitanyi, An Introduction to KolmogorovComplexity and Its Applications, Springer-Verlag, New York, 1997 (2nd Edition).
15. A. Kh. Shen, The concept of (a,/3)-stochasticity in the Kolmogorov sense, and its properties, Soviet Math. Dokl., 28:1(1983), 295-299.
16. A. Kh. Shen, Discussion on Kolmogorov complexity and statistical analysis, The Computer Journal, 42:4(1999), 340-342.
17. N.K. Vereshchagin and P.M.B. Vitanyi, Kolmogorov's structure functions and an application to the foundations of model selection, Proc. 47th IEEE Symp. Found. Comput. Sci., 2002. Full version: http://xxx.lanl.gov/abs/cs.CC/0204037
18. P.M.B. Vitanyi and M. Li, Minimum Description Length Induction, Bayesianism, and Kolmogorov Complexity, IEEE Trans. Inform. Theory, IT-46:2(2000), 446-464. (Pubitemid 30582722)
19. V.V. V'yugin, On the defect of randomness of a finite object with respect to measures with given complexity bounds, SIAM Theory Probab. Appl., 32:3(1987), 508-512.
20. V.V. V'yugin, Algorithmic complexity and stochastic properties of finite binary sequences, The Computer Journal, 42:4(1999), 294-317.