Infinite Swapping using IID Samples
Abstract
We propose a new method for estimating rare event probabilities when independent samples are available. It is assumed that the underlying probability measures satisfy a large deviation principle with a scaling parameter ε that we call temperature. We show how by combining samples at different temperatures, one can construct an estimator with greatly reduced variance. Although as presented here the method is not as broadly applicable as other rare event simulation methods, such as splitting or importance sampling, it does not require any problem-dependent constructions.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 18, 2019
- Source ID
- 10.1145/3317605
Entities
People
- Guo-Jhen Wu
- Michael Snarski
- Paul Dupuis
Organizations
- Brown University
- Defense Advanced Research Projects Agency
- National Science Foundation
- Natural Sciences and Engineering Research Council
- Wind Energy Technologies Office