Jorma Rissanen

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We review the principles of Minimum Description Length and Stochastic Complexity as used in data compression and statistical modeling. Stochastic complexity is formulated as the solution to optimum universal coding problems extending Shannon’s basic source coding theorem. The normalized maximized likelihood, mixture, and predictive codings are each shown to(More)
A connection between universal codes and the problems of prediction and statistical estimation is established. A‘known lower bound for the mean length of universal codes is sharpened and generalized, and optimum universal codes constructed. The bound is defined to give the information in strings relative to the considered class of processes. The earlier(More)
Trans. Inform. Theory, vol. IT-23, pp. 343-353, May 1977. [4] H. H. Tan, “Tree encoding of discrete-time abstract-alphabet stationary block-ergodic sources with a fidelity criterion,” IEEE Trans. Inform. Theory, vol. IT-22, pp. 671-681, Nov. 1976. [5] R. M. Gray, D. L. Net&off, and J. K. Omura, “Process definition of distortion-rate functions and source(More)
The so-called denoising problem, relative to normal models for noise, is formalized such that`noise' is deened as the incompressible part in the data while the compressible part deenes the meaningful information bearing signal. Such a decomposition is eeected by minimization of the ideal code length, called for by the Minimum Description Length (MDL)(More)