The tail wags the distribution: Only sample the tails for efficient reliability analysis

Fuente: arXiv
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Auteurs principaux: Chakroborty, Promit, Shields, Michael D.
Format: Preprint
Publié: 2025
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author Chakroborty, Promit
Shields, Michael D.
author_facet Chakroborty, Promit
Shields, Michael D.
contents To ensure that real-world infrastructure is safe and durable, systems are designed to not fail for any but the most rarely occurring parameter values. By only happening deep in the tails of the parameter distribution, failure probabilities are kept small. At the same time, it is essential to understand the risk associated with the failure of a system, no matter how unlikely. However, estimating such small failure probabilities is challenging; numerous system performance evaluations are necessary to produce even a single system state corresponding to failure, and each such evaluation is usually significantly computationally expensive. To alleviate this difficulty, we propose the Tail Stratified Sampling (TSS) estimator - an intuitive stratified sampling estimator for the failure probability that successively refines the tails of the system parameter distribution, enabling direct sampling of the tails, where failure is expected to occur. The most general construction of TSS is presented, highlighting its versatility and robustness for a variety of applications. The intuitions behind the formulation are explained, followed by a discussion of the theoretical and practical benefits of the method. Various details of the implementation are presented. The performance of the algorithm is then showcased through a host of analytical examples with varying failure domain geometries and failure probabilities as well as multiple numerical case studies of moderate and high dimensionality. To conclude, a qualitative comparison of TSS against the existing foundational variance-reduction methods for reliability analysis is presented, along with suggestions for future developments.
format Preprint
id arxiv_https___arxiv_org_abs_2505_18510
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle The tail wags the distribution: Only sample the tails for efficient reliability analysis
Chakroborty, Promit
Shields, Michael D.
Methodology
62-08, 62D99, 65C05, 62F10, 62H12, 62P30, 90B25
To ensure that real-world infrastructure is safe and durable, systems are designed to not fail for any but the most rarely occurring parameter values. By only happening deep in the tails of the parameter distribution, failure probabilities are kept small. At the same time, it is essential to understand the risk associated with the failure of a system, no matter how unlikely. However, estimating such small failure probabilities is challenging; numerous system performance evaluations are necessary to produce even a single system state corresponding to failure, and each such evaluation is usually significantly computationally expensive. To alleviate this difficulty, we propose the Tail Stratified Sampling (TSS) estimator - an intuitive stratified sampling estimator for the failure probability that successively refines the tails of the system parameter distribution, enabling direct sampling of the tails, where failure is expected to occur. The most general construction of TSS is presented, highlighting its versatility and robustness for a variety of applications. The intuitions behind the formulation are explained, followed by a discussion of the theoretical and practical benefits of the method. Various details of the implementation are presented. The performance of the algorithm is then showcased through a host of analytical examples with varying failure domain geometries and failure probabilities as well as multiple numerical case studies of moderate and high dimensionality. To conclude, a qualitative comparison of TSS against the existing foundational variance-reduction methods for reliability analysis is presented, along with suggestions for future developments.
title The tail wags the distribution: Only sample the tails for efficient reliability analysis
topic Methodology
62-08, 62D99, 65C05, 62F10, 62H12, 62P30, 90B25
url https://arxiv.org/abs/2505.18510