A novel adaptive importance sampling algorithm based on Markov chain and low-discrepancy sequence

A novel adaptive importance sampling method is proposed to estimate the structural failure probability. It properly utilizes Markov chain algorithm to form an adaptive importance sampling procedure. The main concept is suggesting the proposal distributions of Markov chain as the importance sampling...

Full description

Saved in:
Bibliographic Details
Published in:Aerospace science and technology Vol. 29; no. 1; pp. 253 - 261
Main Authors: Yuan, Xiukai, Lu, Zhenzhou, Zhou, Changcong, Yue, Zhufeng
Format: Journal Article
Language:English
Published: Issy-les-Moulineaux Elsevier Masson SAS 01.08.2013
Elsevier
Subjects:
Adaptive algorithms
Algorithms
Density
Estimates
Exact sciences and technology
Failure
Importance sampling
Low-discrepancy sequence
Markov chain
Markov chains
Markov processes
Mathematical methods in physics
Monte Carlo simulation
Physics
Probability theory, stochastic processes, and statistics
Reliability
Structural failure
Markov chain
Low-discrepancy sequence
Reliability
Monte Carlo simulation
Importance sampling
Markov process
Monte Carlo methods
Algorithms
Digital simulation
Modelling
Adaptive method
ISSN:1270-9638, 1626-3219
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A novel adaptive importance sampling method is proposed to estimate the structural failure probability. It properly utilizes Markov chain algorithm to form an adaptive importance sampling procedure. The main concept is suggesting the proposal distributions of Markov chain as the importance sampling density. Markov chain states can adaptively populate the important failure regions thus the importance sampling based on them will yield an efficient and accurate estimate of the failure probability. Compared with existent methods, it does not need the solution of the design point(s) or the pre-sampling in the failure region. Various examples are given to demonstrate the advantages of the proposed method.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1270-9638
1626-3219
DOI:10.1016/j.ast.2013.03.008