000013400 001__ 13400
000013400 005__ 20161114160339.0
000013400 04107 $$aeng
000013400 046__ $$k2009-06-22
000013400 100__ $$aZuev K., M.
000013400 24500 $$aModified metropolis-hastings algorithm with delayed rejection for high-dimensional reliability analysis

000013400 24630 $$n2.$$pComputational Methods in Structural Dynamics and Earhquake Engineering
000013400 260__ $$bNational Technical University of Athens, 2009
000013400 506__ $$arestricted
000013400 520__ $$2eng$$aThe development of an efficient MCMC strategy for sampling from complex distributions is a difficult task that needs to be solved for calculating small failure probabilities encountered in high-dimensional reliability analysis of engineering systems. Usually different variations of the Metropolis-Hastings algorithm (MH) are used. However, the standard MH algorithm does generally not work in high dimensions, since it leads to very frequent repeated samples. In order to overcome this deficiency one can use the Modified Metropolis-Hastings algorithm (MMH) proposed in [1]. Another variation of the MH algorithm, called MetropolisHastings algorithm with delayed rejection (MHDR) has been proposed by [2]. The key idea behind the MHDR algorithm is to reduce the correlation between states of the Markov chain. In this paper we combine the ideas of MMH and MHDR and propose a novel modification of the MH algorithm, called Modified Metropolis-Hastings algorithm with delayed rejection (MMHDR). The efficiency of the new algorithm is demonstrated with a numerical example where MMHDR is used together with Subset simulation for computing small failure probabilities in high dimensions.

000013400 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000013400 653__ $$aReliability, Subset simulation, Markov chain Monte Carlo, Metropolis-Hastings algorithm. Abstract. The development of an efficient MCMC strategy for sampling from complex distributions is a difficult task that needs to be solved for calculating small failure probabilities encountered in high-dimensional reliability analysis of engineering systems. Usually different variations of the Metropolis-Hastings algorithm (MH) are used. However, the standard MH algorithm does generally not work in high dimensions, since it leads to very frequent repeated samples. In order to overcome this deficiency one can use the Modified Metropolis-Hastings algorithm (MMH) proposed in [1]. Another variation of the MH algorithm, called MetropolisHastings algorithm with delayed rejection (MHDR) has been proposed by [2]. The key idea behind the MHDR algorithm is to reduce the correlation between states of the Markov chain. In this paper we combine the ideas of MMH and MHDR and propose a novel modification of the MH algorithm, called Modified Metropolis-Hastings algorithm with delayed rejection (MMHDR). The efficiency of the new algorithm is demonstrated with a numerical example where MMHDR is used together with Subset simulation for computing small failure probabilities in high dimensions.

000013400 7112_ $$aCOMPDYN 2009 - 2nd International Thematic Conference$$cIsland of Rhodes (GR)$$d2009-06-22 / 2009-06-24$$gCOMPDYN2009
000013400 720__ $$aZuev K., M.$$iKatafygiotis L., S.
000013400 8560_ $$ffischerc@itam.cas.cz
000013400 8564_ $$s153508$$uhttps://invenio.itam.cas.cz/record/13400/files/CD721.pdf$$yOriginal version of the author's contribution as presented on CD, section: Robust stochastic analysis, optimal design and model updating of engineering systems - i (MS).
000013400 962__ $$r13074
000013400 980__ $$aPAPER