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|a eng
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|a Li, Genyuan
|e verfasserin
|4 aut
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|a Correlation method for variance reduction of Monte Carlo integration in RS-HDMR
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|c 2003
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|a Text
|b txt
|2 rdacontent
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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|a Band
|b nc
|2 rdacarrier
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|a Date Completed 07.07.2003
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|a Date Revised 04.11.2003
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a Copyright 2003 Wiley Periodicals, Inc. J Comput Chem 24: 277-283, 2003
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|a The High Dimensional Model Representation (HDMR) technique is a procedure for efficiently representing high-dimensional functions. A practical form of the technique, RS-HDMR, is based on randomly sampling the overall function and utilizing orthonormal polynomial expansions. The determination of expansion coefficients employs Monte Carlo integration, which controls the accuracy of RS-HDMR expansions. In this article, a correlation method is used to reduce the Monte Carlo integration error. The determination of the expansion coefficients becomes an iteration procedure, and the resultant RS-HDMR expansion has much better accuracy than that achieved by direct Monte Carlo integration. For an illustration in four dimensions a few hundred random samples are sufficient to construct an RS-HDMR expansion by the correlation method with an accuracy comparable to that obtained by direct Monte Carlo integration with thousands of samples
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|a Journal Article
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|a Rabitz, Herschel
|e verfasserin
|4 aut
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|a Wang, Sheng-Wei
|e verfasserin
|4 aut
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|a Georgopoulos, Panos G
|e verfasserin
|4 aut
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 24(2003), 3 vom: 15. Feb., Seite 277-83
|w (DE-627)NLM098138448
|x 0192-8651
|7 nnns
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|g volume:24
|g year:2003
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|g day:15
|g month:02
|g pages:277-83
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