Strongly consistent code-based identification and order estimation for constrained finite-state model classes
作者： J.C. Kieffer
DOI: 10.1109/18.256497
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摘要: Observations are made of data generated by a stationary ergodic finite-alphabet information source according to an unknown statistical model. Two modeling problems, the identification problem and order estimation problem, considered. It is required that given model class in each be constrained finite-state class, which type includes many classes information-theoretic interest. Strongly consistent decision rules exhibited both problem. The code-based chosen based on how well certain code for encodes observed data. used maximum likelihood asymptotic performance gauged means key property divergence-rate distance. >
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