Work

Sequential Monte Carlo samplers for rare events

Year: 2006

Type: article

Abstract: . We present novel sequential Monte Carlo (SMC) algorithms for the simulation of two broad classes of rare events which are suitable for the estimation of tail probabilities and probability density functions in the regions of rare events, as well as the simulation of rare system trajectories. These methods have some connection with previously proposed importance sampling (IS) and interacting particle system (IPS) methodologies, particularly those of [8, 4], but di.er significantly from previous approaches in a number of respects: especially in that they operate directly on the path space of the Markov process of interest.

DOI: 10.82444/warw.33606514

Authors: Adam M. Johansen, Pierre Del Moral, Arnaud Doucet

Language: English

Topic: Markov Chains and Monte Carlo Methods

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W1562576911

FWCI: 1.444

Cites: 16

Cited by: 37

Related to: 10