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A Comparison of Markov Chain Methods for Reliability Estimation
Author(s) -
Proppe Carsten
Publication year - 2017
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201710332
Subject(s) - markov chain , reliability (semiconductor) , markov chain monte carlo , computer science , monte carlo method , estimation , sample (material) , sample size determination , reliability engineering , algorithm , statistics , mathematical optimization , mathematics , machine learning , engineering , chemistry , physics , chromatography , power (physics) , systems engineering , quantum mechanics
Two Markov chain Monte Carlo simulation methods for reliability estimation, subset simulation and the moving particles algorithm, are compared based on theoretical arguments and test cases. The differences in the efficiency between both algorithms are rather small. They seem to be well suited for off‐the‐shelf reliability estimations, but with a different setting for the most important parameters (proposal density and initial sample size). (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)