Importance Sampling Strategy for Heavy-Tailed Systems with Catastrophe Principle

Xingyu Wang*, Chang Han Rhee

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Large deviations theory has a long history of providing powerful machinery for designing efficient rare event simulation techniques. However, traditional large deviations theory fails to provide useful bounds in heavy-tailed contexts, and designing efficient rare-event simulation algorithms for heavy-tailed systems has been considered challenging. Recent developments in the theory of heavy-tailed large deviations enable designing a strongly efficient importance sampling scheme that is universally applicable to a wide range of rare events. This tutorial aims to provide an accessible overview of the recent developments in the large deviations theory for heavy-tailed stochastic processes, which is followed by a detailed account of the design principle behind the strongly efficient importance sampling scheme for such processes. The implementations of the general principle are demonstrated through a few specific heavy-tailed rare events that arise in stochastic approximation, finance, and queueing theory contexts.

Original languageEnglish (US)
Title of host publication2023 Winter Simulation Conference, WSC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages76-90
Number of pages15
ISBN (Electronic)9798350369663
DOIs
StatePublished - 2023
Event2023 Winter Simulation Conference, WSC 2023 - San Antonio, United States
Duration: Dec 10 2023Dec 13 2023

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736

Conference

Conference2023 Winter Simulation Conference, WSC 2023
Country/TerritoryUnited States
CitySan Antonio
Period12/10/2312/13/23

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

Cite this