Splitting for rare event simulation

Paul Glasserman · Philip Heidelberger · Perwez Shahabuddin · Tim R. Zajic

77 citations0/0 files storedWinter Simulation Conference

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Simulation Techniques and Applications Probability and Risk Models Markov Chains and Monte Carlo Methods

Abstract

An approach to rare event simulation uses the technique of splitting. The basic idea is to split sample paths of the stochastic process into multiple copies when they approach closer to the rare set; this increases the overall number of hits to the rare set for a given amount of simulation time. This paper analyzes the bias and efficiency of some simple cases of this method.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 77 citations exist upstream.

References

1995Fast simulation of rare events in queueing and reliability models 1992The Asymptotic Efficiency of Simulation Estimators 1994Restart: a straightforward method for fast simulation of rare events

Cited by

1998A large deviations perspective on the efficiency of multilevel splitting 2014A survey of rare event simulation methods for static input–output models 2002On the importance function in splitting simulation 1997Cloning 1998A comparison of RESTART implementations 2006On the efficiency of RESTART for multidimensional state systems 1998Simulation of rare events in communications networks 2014Rare Event Probability Estimation in the Presence of Epistemic Uncertainty on Input Probability Distribution Parameters

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.