A radial version of the Central Limit Theorem
Kunal Narayan Chaudhury
TL;DR
This paper offers a new probabilistic interpretation of the Central Limit Theorem specifically applied to approximating isotropic Gaussian distributions.
Contribution
It introduces a radial perspective on the CLT, providing novel insights into Gaussian approximation methods.
Findings
Provides a new probabilistic interpretation of the CLT
Enhances understanding of Gaussian approximation techniques
Offers potential for improved statistical modeling
Abstract
In this note, we give a probabilistic interpretation of the Central Limit Theorem used for approximating isotropic Gaussians in [1].
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Taxonomy
TopicsGaussian Processes and Bayesian Inference · Statistical and numerical algorithms · Image and Signal Denoising Methods