MCMC algorithms for Subset Simulation

Iason Papaioannou, Wolfgang Betz, Kilian Zwirglmaier, Daniel Straub

Research output: Contribution to journalArticlepeer-review

308 Scopus citations

Abstract

Abstract Subset Simulation is an adaptive simulation method that efficiently solves structural reliability problems with many random variables. The method requires sampling from conditional distributions, which is achieved through Markov Chain Monte Carlo (MCMC) algorithms. This paper discusses different MCMC algorithms proposed for Subset Simulation and introduces a novel approach for MCMC sampling in the standard normal space. Two variants of the algorithm are proposed: a basic variant, which is simpler than existing algorithms with equal accuracy and efficiency, and a more efficient variant with adaptive scaling. It is demonstrated that the proposed algorithm improves the accuracy of Subset Simulation, without the need for additional model evaluations.

Original languageEnglish
Article number2844
Pages (from-to)89-103
Number of pages15
JournalProbabilistic Engineering Mechanics
Volume41
DOIs
StatePublished - 6 Jul 2015

Keywords

  • Adaptive scaling
  • Conditional sampling
  • High dimensions
  • MCMC
  • Reliability analysis
  • Subset Simulation

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