Towards zero variance estimators for rare event probabilities - Sorbonne Université Access content directly
Journal Articles ACM Transactions on Modeling and Computer Simulation Year : 2013

Towards zero variance estimators for rare event probabilities

Abstract

Improving Importance Sampling estimators for rare event probabilities requires sharp approximations of conditional densities. This is achieved for events $E_{n}:=\left( u(X_{1})+...+u(X_{n}\right) )\in A_{n}$ where the summands are i.i.d. and $E_{n}$ is a large or moderate deviation event. The approximation of the conditional density of the vector $\left( X_{1},...,X_{k_{n}}\right) $ with respect to $E_{n}$ on long runs, when $% k_{n}/n\rightarrow1$, is handled. The maximal value of $k_{n}$ compatible with a given accuracy is discussed; simulated results are presented, which enlight the gain of the present approach over classical IS schemes. Detailed algorithms are proposed.
Fichier principal
Vignette du fichier
Towards_Zero.pdf (252.35 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-00613346 , version 1 (04-08-2011)
hal-00613346 , version 2 (03-02-2012)

Identifiers

  • HAL Id : hal-00613346 , version 2

Cite

Michel Broniatowski, Virgile Caron. Towards zero variance estimators for rare event probabilities. ACM Transactions on Modeling and Computer Simulation, 2013, 23 (1), pp. 7, 23. ⟨hal-00613346v2⟩
109 View
88 Download

Share

Gmail Mastodon Facebook X LinkedIn More