Kolmogorov Complexity, Data Compression, and Inference
作者： Thomas M. Cover
DOI: 10.1007/978-94-009-5113-6_2
关键词:
摘要: If a sequence of random variables has Shannon entropy H, it is well known that there exists an efficient description this which requires only H bits. But the also to do with inference. Low sequences allow good guesses their next terms. This best illustrated by allowing gambler gamble at fair odds on such sequence. The amount money one can make essentially complement respect length
springer.com
sci-hub.st 参考文章(6)
C. P. Schnorr, A unified approach to the definition of random sequences Theory of Computing Systems \/ Mathematical Systems Theory. ,vol. 5, pp. 246- 258 ,(1971) , 10.1007/BF01694181
Gregory J. Chaitin, A Theory of Program Size Formally Identical to Information Theory Journal of the ACM. ,vol. 22, pp. 329- 340 ,(1975) , 10.1145/321892.321894
A K Zvonkin, L A Levin, THE COMPLEXITY OF FINITE OBJECTS AND THE DEVELOPMENT OF THE CONCEPTS OF INFORMATION AND RANDOMNESS BY MEANS OF THE THEORY OF ALGORITHMS Russian Mathematical Surveys. ,vol. 25, pp. 83- 124 ,(1970) , 10.1070/RM1970V025N06ABEH001269
R.J. Solomonoff, A formal theory of inductive inference. Part II Information and Control. ,vol. 7, pp. 224- 254 ,(1964) , 10.1016/S0019-9958(64)90131-7
S Leung-Yan-Cheong, T Cover, None, Some equivalences between Shannon entropy and Kolmogorov complexity IEEE Transactions on Information Theory. ,vol. 24, pp. 331- 338 ,(1978) , 10.1109/TIT.1978.1055891
R.J. Solomonoff, A formal theory of inductive inference. Part I Information and Control. ,vol. 7, pp. 1- 22 ,(1964) , 10.1016/S0019-9958(64)90223-2