Fundamental Limitations in Sequential Prediction and Recursive Algorithms: L_p Bounds via an Entropic Analysis
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In this paper, we obtain fundamental L_p bounds in sequential prediction and recursive algorithms via an entropic analysis. Both classes of problems are examined by investigating the underlying entropic relationships of the data and/or noises involved, and the derived lower bounds may all be quantified in a conditional entropy characterization. We also study the conditions to achieve the generic bounds from an innovations' viewpoint.
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