Fundamental Limitations in Sequential Prediction and Recursive Algorithms: $\mathcal{L}_{p}$ Bounds via an Entropic Analysis
Machine Learning
2021-05-12 v2 Information Theory
Signal Processing
math.IT
Statistics Theory
Machine Learning
Statistics Theory
Abstract
In this paper, we obtain fundamental 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.
Cite
@article{arxiv.1912.02628,
title = {Fundamental Limitations in Sequential Prediction and Recursive Algorithms: $\mathcal{L}_{p}$ Bounds via an Entropic Analysis},
author = {Song Fang and Quanyan Zhu},
journal= {arXiv preprint arXiv:1912.02628},
year = {2021}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1910.06742. text overlap with arXiv:1912.05541