Robust Estimation of Mean Values

TL;DR

This paper presents a computational method for estimating mean values under uncertainty, providing efficient bounds calculation using a sample reuse technique with Poisson-distributed complexity.

Contribution

It introduces a novel sample reuse approach for efficiently computing bounds of mean values in uncertain environments.

Findings

Bounds are efficiently computable under mild assumptions.

The computational complexity follows a Poisson distribution.

The method improves estimation efficiency in uncertain scenarios.

Abstract

In this paper, we develop a computational approach for estimating the mean value of a quantity in the presence of uncertainty. We demonstrate that, under some mild assumptions, the upper and lower bounds of the mean value are efficiently computable via a sample reuse technique, of which the computational complexity is shown to posses a Poisson distribution.