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Publication
IEEE Trans. Inf. Theory
Paper
Fisher information and stochastic complexity
Abstract
By taking into account the Fisher information and removing an inherent redundancy in earlier two-part codes, a sharper code length as the stochastic complexity and the associated universal process are derived for a class of parametric processes. The main condition required is that the maximum-likelihood estimates satisfy the Central Limit Theorem. The same code length is also obtained from the so-called maximum-likelihood code. © 1996 IEEE.