An island particle Markov chain Monte Carlo algorithm for safety analysis
VERGÉ, Christelle
Centre National d'Études Spatiales [Toulouse] [CNES]
ONERA - The French Aerospace Lab [Châtillon]
Advanced Learning Evolutionary Algorithms [ALEA]
Centre National d'Études Spatiales [Toulouse] [CNES]
ONERA - The French Aerospace Lab [Châtillon]
Advanced Learning Evolutionary Algorithms [ALEA]
DEL MORAL, Pierre
Advanced Learning Evolutionary Algorithms [ALEA]
Institut de Mathématiques de Bordeaux [IMB]
Advanced Learning Evolutionary Algorithms [ALEA]
Institut de Mathématiques de Bordeaux [IMB]
VERGÉ, Christelle
Centre National d'Études Spatiales [Toulouse] [CNES]
ONERA - The French Aerospace Lab [Châtillon]
Advanced Learning Evolutionary Algorithms [ALEA]
Centre National d'Études Spatiales [Toulouse] [CNES]
ONERA - The French Aerospace Lab [Châtillon]
Advanced Learning Evolutionary Algorithms [ALEA]
DEL MORAL, Pierre
Advanced Learning Evolutionary Algorithms [ALEA]
Institut de Mathématiques de Bordeaux [IMB]
< Leer menos
Advanced Learning Evolutionary Algorithms [ALEA]
Institut de Mathématiques de Bordeaux [IMB]
Idioma
en
Article de revue
Este ítem está publicado en
Structural Safety. 2013-06
Elsevier
Resumen en inglés
Estimating rare event probability with accuracy is of great interest for safety and reliability applications. Nevertheless, some simulation parameters such as the input density parameters in the case of input-output ...Leer más >
Estimating rare event probability with accuracy is of great interest for safety and reliability applications. Nevertheless, some simulation parameters such as the input density parameters in the case of input-output functions, are often set for simplification reasons. A bad estimation of the parameters can strongly modify rare event probability estimations. In the present article, we design a new island particle Markov chain Monte Carlo algorithm to determine the parameters that, in case of bad estimation, tend to increase the rare event probability value. This algorithm also gives an estimate of the rare event probability maximum taking into account the likelihood of the parameter. The principles of this statistical technique are described throughout this article and its results are analyzed on different test cases.< Leer menos
Palabras clave en inglés
rare event
sequential Monte Carlo
particle filtering
sensitivity analysis
Orígen
Importado de HalCentros de investigación