Non-Gaussian simulation using Hermite polynomial expansion: convergences and algorithms

Mathematical justifications are given for a Monte Carlo simulation technique based on memoryless transformations of Gaussian processes. Different types of convergences are given for the approaching sequence. Moreover an original numerical method is proposed in order to solve the functional equation...

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Veröffentlicht in:	Probabilistic engineering mechanics 2002-07, Vol.17 (3), p.253-264
Hauptverfasser:	Puig, Bénédicte, Poirion, Fabrice, Soize, Christian
Format:	Artikel
Sprache:	eng
Schlagworte:	Engineering Sciences Exact sciences and technology Hermite polynomial expansion Mathematics Mechanics Monte Carlo simulation Non-Gaussian process Probability Probability and statistics Probability theory and stochastic processes Sciences and techniques of general use Statistics Stochastic processes
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Beschreibung
Zusammenfassung:	Mathematical justifications are given for a Monte Carlo simulation technique based on memoryless transformations of Gaussian processes. Different types of convergences are given for the approaching sequence. Moreover an original numerical method is proposed in order to solve the functional equation yielding the underlying Gaussian process autocorrelation function.
ISSN:	0266-8920 1878-4275
DOI:	10.1016/S0266-8920(02)00010-3