In this paper, we obtain fundamental $\mathcal{L}_{p}$ 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.
翻译:在本文中,我们通过一种昆虫分析在连续预测和递归算法中获得了基本值$mathcal{L ⁇ p} 和 $。 通过调查所涉数据和(或)噪音的内在的昆虫关系来研究这两类问题,而衍生的下限都可以在有条件的诱变特征中量化。我们还从创新的角度研究达到通用值的条件。