Random uniform approximation under weighted importance sampling of a
 class of stratified input

Q: What type of study is this?

This is a Quantitative Study study.

Jun Xian; Xiaoda Xu

doi:10.48550/arxiv.2505.09312

May 14, 2025Open Access

Random uniform approximation under weighted importance sampling of a class of stratified input

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Key Points

Upper bounds for Lp-discrepancy under weighted importance sampling were derived, providing crucial insights.
The expected discrepancy contributes to improved error estimates for integral approximations using stratified sampling.
This approach is particularly relevant for various applications that rely on accurate statistical sampling methods.
Our findings reveal that stratified input patterns can effectively minimize discrepancies in random approximations.

Implication

Analysis shows upper bounds for Lp-discrepancy in stratified sampling, implicating improved error estimates.

Abstract

We consider random discrepancy under weighted importance sampling of a class of stratified input. We give the expected Lₚ-discrepancy(2≤ p<∞) upper bound in weighted form under a class of stratified sampling. This result contributes to the error estimate of the upper bound of the integral approximation under weighted importance sampling, and and our sampling pattern is a stratified input.

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Cite This Study

Xian et al. (2025) studied this question.

synapsesocial.com/papers/68e82b12e7fc21a3005004a2 https://doi.org/10.48550/arxiv.2505.09312

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Random uniform approximation under weighted importance sampling of a class of stratified input

Key Points

Implication

Abstract

Discussion

Cite This Study

Random uniform approximation under weighted importance sampling of a class of stratified input

Key Points

Implication

Abstract

Discussion

Cite This Study

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