Random Number Generation from Right-Skewed, Symmetric, and Left-Skewed Distributions
Monte Carlo simulations have become a mainstream technique for environmental and technical risk assessments. Because their results are dependent on the quality of the involved input distributions, it is important to identify distributions that are flexible enough to model all relevant data yet effic...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Voit, Eberhard O. [verfasserIn] Schwacke, Lorelei H. [verfasserIn] |
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| Format: |
E-Artikel |
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| Erschienen: |
Boston, USA and Oxford, UK: Blackwell Publishers Inc. ; 2000 |
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| Schlagwörter: |
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| Umfang: |
Online-Ressource |
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| Reproduktion: |
2002 ; Blackwell Publishing Journal Backfiles 1879-2005 |
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| Übergeordnetes Werk: |
In: Risk analysis - Oxford [u.a.] : Wiley-Blackwell, 1981, 20(2000), 1, Seite 0 |
| Übergeordnetes Werk: |
volume:20 ; year:2000 ; number:1 ; pages:0 |
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| DOI / URN: |
10.1111/0272-4332.00006 |
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| 520 | |a Monte Carlo simulations have become a mainstream technique for environmental and technical risk assessments. Because their results are dependent on the quality of the involved input distributions, it is important to identify distributions that are flexible enough to model all relevant data yet efficient enough to allow thousands of evaluations necessary in a typical simulation analysis. It has been shown in recent years that the S-distribution provides accurate representations for frequency data that are symmetric or skewed to either side. This flexibility makes the S-distribution an ideal candidate for Monte Carlo analyses. To use the distribution effectively, methods must be available for drawing S-distributed random numbers. Such a method is proposed here. It is shown that S-distributed random numbers can be efficiently generated from a simple algebraic formula whose coefficients are tabulated. The method is shown step by step and illustrated with a detailed example. (The tables are accessible in electronic form in the FTP parent directory at http://www.musc.edu/ | ||
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10.1111/0272-4332.00006 doi (DE-627)NLEJ24373302X DE-627 ger DE-627 rakwb Voit, Eberhard O. verfasserin aut Random Number Generation from Right-Skewed, Symmetric, and Left-Skewed Distributions Boston, USA and Oxford, UK Blackwell Publishers Inc. 2000 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Monte Carlo simulations have become a mainstream technique for environmental and technical risk assessments. Because their results are dependent on the quality of the involved input distributions, it is important to identify distributions that are flexible enough to model all relevant data yet efficient enough to allow thousands of evaluations necessary in a typical simulation analysis. It has been shown in recent years that the S-distribution provides accurate representations for frequency data that are symmetric or skewed to either side. This flexibility makes the S-distribution an ideal candidate for Monte Carlo analyses. To use the distribution effectively, methods must be available for drawing S-distributed random numbers. Such a method is proposed here. It is shown that S-distributed random numbers can be efficiently generated from a simple algebraic formula whose coefficients are tabulated. The method is shown step by step and illustrated with a detailed example. (The tables are accessible in electronic form in the FTP parent directory at http://www.musc.edu/ 2002 Blackwell Publishing Journal Backfiles 1879-2005 |2002|||||||||| Monte Carlo simulation Schwacke, Lorelei H. verfasserin aut In Risk analysis Oxford [u.a.] : Wiley-Blackwell, 1981 20(2000), 1, Seite 0 Online-Ressource (DE-627)NLEJ243926847 (DE-600)2001458-2 1539-6924 nnns volume:20 year:2000 number:1 pages:0 http://dx.doi.org/10.1111/0272-4332.00006 text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 20 2000 1 0 |
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10.1111/0272-4332.00006 doi (DE-627)NLEJ24373302X DE-627 ger DE-627 rakwb Voit, Eberhard O. verfasserin aut Random Number Generation from Right-Skewed, Symmetric, and Left-Skewed Distributions Boston, USA and Oxford, UK Blackwell Publishers Inc. 2000 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Monte Carlo simulations have become a mainstream technique for environmental and technical risk assessments. Because their results are dependent on the quality of the involved input distributions, it is important to identify distributions that are flexible enough to model all relevant data yet efficient enough to allow thousands of evaluations necessary in a typical simulation analysis. It has been shown in recent years that the S-distribution provides accurate representations for frequency data that are symmetric or skewed to either side. This flexibility makes the S-distribution an ideal candidate for Monte Carlo analyses. To use the distribution effectively, methods must be available for drawing S-distributed random numbers. Such a method is proposed here. It is shown that S-distributed random numbers can be efficiently generated from a simple algebraic formula whose coefficients are tabulated. The method is shown step by step and illustrated with a detailed example. (The tables are accessible in electronic form in the FTP parent directory at http://www.musc.edu/ 2002 Blackwell Publishing Journal Backfiles 1879-2005 |2002|||||||||| Monte Carlo simulation Schwacke, Lorelei H. verfasserin aut In Risk analysis Oxford [u.a.] : Wiley-Blackwell, 1981 20(2000), 1, Seite 0 Online-Ressource (DE-627)NLEJ243926847 (DE-600)2001458-2 1539-6924 nnns volume:20 year:2000 number:1 pages:0 http://dx.doi.org/10.1111/0272-4332.00006 text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 20 2000 1 0 |
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10.1111/0272-4332.00006 doi (DE-627)NLEJ24373302X DE-627 ger DE-627 rakwb Voit, Eberhard O. verfasserin aut Random Number Generation from Right-Skewed, Symmetric, and Left-Skewed Distributions Boston, USA and Oxford, UK Blackwell Publishers Inc. 2000 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Monte Carlo simulations have become a mainstream technique for environmental and technical risk assessments. Because their results are dependent on the quality of the involved input distributions, it is important to identify distributions that are flexible enough to model all relevant data yet efficient enough to allow thousands of evaluations necessary in a typical simulation analysis. It has been shown in recent years that the S-distribution provides accurate representations for frequency data that are symmetric or skewed to either side. This flexibility makes the S-distribution an ideal candidate for Monte Carlo analyses. To use the distribution effectively, methods must be available for drawing S-distributed random numbers. Such a method is proposed here. It is shown that S-distributed random numbers can be efficiently generated from a simple algebraic formula whose coefficients are tabulated. The method is shown step by step and illustrated with a detailed example. (The tables are accessible in electronic form in the FTP parent directory at http://www.musc.edu/ 2002 Blackwell Publishing Journal Backfiles 1879-2005 |2002|||||||||| Monte Carlo simulation Schwacke, Lorelei H. verfasserin aut In Risk analysis Oxford [u.a.] : Wiley-Blackwell, 1981 20(2000), 1, Seite 0 Online-Ressource (DE-627)NLEJ243926847 (DE-600)2001458-2 1539-6924 nnns volume:20 year:2000 number:1 pages:0 http://dx.doi.org/10.1111/0272-4332.00006 text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 20 2000 1 0 |
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10.1111/0272-4332.00006 doi (DE-627)NLEJ24373302X DE-627 ger DE-627 rakwb Voit, Eberhard O. verfasserin aut Random Number Generation from Right-Skewed, Symmetric, and Left-Skewed Distributions Boston, USA and Oxford, UK Blackwell Publishers Inc. 2000 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Monte Carlo simulations have become a mainstream technique for environmental and technical risk assessments. Because their results are dependent on the quality of the involved input distributions, it is important to identify distributions that are flexible enough to model all relevant data yet efficient enough to allow thousands of evaluations necessary in a typical simulation analysis. It has been shown in recent years that the S-distribution provides accurate representations for frequency data that are symmetric or skewed to either side. This flexibility makes the S-distribution an ideal candidate for Monte Carlo analyses. To use the distribution effectively, methods must be available for drawing S-distributed random numbers. Such a method is proposed here. It is shown that S-distributed random numbers can be efficiently generated from a simple algebraic formula whose coefficients are tabulated. The method is shown step by step and illustrated with a detailed example. (The tables are accessible in electronic form in the FTP parent directory at http://www.musc.edu/ 2002 Blackwell Publishing Journal Backfiles 1879-2005 |2002|||||||||| Monte Carlo simulation Schwacke, Lorelei H. verfasserin aut In Risk analysis Oxford [u.a.] : Wiley-Blackwell, 1981 20(2000), 1, Seite 0 Online-Ressource (DE-627)NLEJ243926847 (DE-600)2001458-2 1539-6924 nnns volume:20 year:2000 number:1 pages:0 http://dx.doi.org/10.1111/0272-4332.00006 text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 20 2000 1 0 |
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10.1111/0272-4332.00006 doi (DE-627)NLEJ24373302X DE-627 ger DE-627 rakwb Voit, Eberhard O. verfasserin aut Random Number Generation from Right-Skewed, Symmetric, and Left-Skewed Distributions Boston, USA and Oxford, UK Blackwell Publishers Inc. 2000 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Monte Carlo simulations have become a mainstream technique for environmental and technical risk assessments. Because their results are dependent on the quality of the involved input distributions, it is important to identify distributions that are flexible enough to model all relevant data yet efficient enough to allow thousands of evaluations necessary in a typical simulation analysis. It has been shown in recent years that the S-distribution provides accurate representations for frequency data that are symmetric or skewed to either side. This flexibility makes the S-distribution an ideal candidate for Monte Carlo analyses. To use the distribution effectively, methods must be available for drawing S-distributed random numbers. Such a method is proposed here. It is shown that S-distributed random numbers can be efficiently generated from a simple algebraic formula whose coefficients are tabulated. The method is shown step by step and illustrated with a detailed example. (The tables are accessible in electronic form in the FTP parent directory at http://www.musc.edu/ 2002 Blackwell Publishing Journal Backfiles 1879-2005 |2002|||||||||| Monte Carlo simulation Schwacke, Lorelei H. verfasserin aut In Risk analysis Oxford [u.a.] : Wiley-Blackwell, 1981 20(2000), 1, Seite 0 Online-Ressource (DE-627)NLEJ243926847 (DE-600)2001458-2 1539-6924 nnns volume:20 year:2000 number:1 pages:0 http://dx.doi.org/10.1111/0272-4332.00006 text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 20 2000 1 0 |
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Random Number Generation from Right-Skewed, Symmetric, and Left-Skewed Distributions |
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Monte Carlo simulations have become a mainstream technique for environmental and technical risk assessments. Because their results are dependent on the quality of the involved input distributions, it is important to identify distributions that are flexible enough to model all relevant data yet efficient enough to allow thousands of evaluations necessary in a typical simulation analysis. It has been shown in recent years that the S-distribution provides accurate representations for frequency data that are symmetric or skewed to either side. This flexibility makes the S-distribution an ideal candidate for Monte Carlo analyses. To use the distribution effectively, methods must be available for drawing S-distributed random numbers. Such a method is proposed here. It is shown that S-distributed random numbers can be efficiently generated from a simple algebraic formula whose coefficients are tabulated. The method is shown step by step and illustrated with a detailed example. (The tables are accessible in electronic form in the FTP parent directory at http://www.musc.edu/ |
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Monte Carlo simulations have become a mainstream technique for environmental and technical risk assessments. Because their results are dependent on the quality of the involved input distributions, it is important to identify distributions that are flexible enough to model all relevant data yet efficient enough to allow thousands of evaluations necessary in a typical simulation analysis. It has been shown in recent years that the S-distribution provides accurate representations for frequency data that are symmetric or skewed to either side. This flexibility makes the S-distribution an ideal candidate for Monte Carlo analyses. To use the distribution effectively, methods must be available for drawing S-distributed random numbers. Such a method is proposed here. It is shown that S-distributed random numbers can be efficiently generated from a simple algebraic formula whose coefficients are tabulated. The method is shown step by step and illustrated with a detailed example. (The tables are accessible in electronic form in the FTP parent directory at http://www.musc.edu/ |
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Monte Carlo simulations have become a mainstream technique for environmental and technical risk assessments. Because their results are dependent on the quality of the involved input distributions, it is important to identify distributions that are flexible enough to model all relevant data yet efficient enough to allow thousands of evaluations necessary in a typical simulation analysis. It has been shown in recent years that the S-distribution provides accurate representations for frequency data that are symmetric or skewed to either side. This flexibility makes the S-distribution an ideal candidate for Monte Carlo analyses. To use the distribution effectively, methods must be available for drawing S-distributed random numbers. Such a method is proposed here. It is shown that S-distributed random numbers can be efficiently generated from a simple algebraic formula whose coefficients are tabulated. The method is shown step by step and illustrated with a detailed example. (The tables are accessible in electronic form in the FTP parent directory at http://www.musc.edu/ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ24373302X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210707185018.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">120427s2000 xx |||||o 00| ||und c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1111/0272-4332.00006</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ24373302X</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Voit, Eberhard O.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Random Number Generation from Right-Skewed, Symmetric, and Left-Skewed Distributions</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Boston, USA and Oxford, UK</subfield><subfield code="b">Blackwell Publishers Inc.</subfield><subfield code="c">2000</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Monte Carlo simulations have become a mainstream technique for environmental and technical risk assessments. Because their results are dependent on the quality of the involved input distributions, it is important to identify distributions that are flexible enough to model all relevant data yet efficient enough to allow thousands of evaluations necessary in a typical simulation analysis. It has been shown in recent years that the S-distribution provides accurate representations for frequency data that are symmetric or skewed to either side. This flexibility makes the S-distribution an ideal candidate for Monte Carlo analyses. To use the distribution effectively, methods must be available for drawing S-distributed random numbers. Such a method is proposed here. It is shown that S-distributed random numbers can be efficiently generated from a simple algebraic formula whose coefficients are tabulated. The method is shown step by step and illustrated with a detailed example. (The tables are accessible in electronic form in the FTP parent directory at http://www.musc.edu/</subfield></datafield><datafield tag="533" ind1=" " ind2=" "><subfield code="d">2002</subfield><subfield code="f">Blackwell Publishing Journal Backfiles 1879-2005</subfield><subfield code="7">|2002||||||||||</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Monte Carlo simulation</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Schwacke, Lorelei H.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Risk analysis</subfield><subfield code="d">Oxford [u.a.] : Wiley-Blackwell, 1981</subfield><subfield code="g">20(2000), 1, Seite 0</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ243926847</subfield><subfield code="w">(DE-600)2001458-2</subfield><subfield code="x">1539-6924</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:20</subfield><subfield code="g">year:2000</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://dx.doi.org/10.1111/0272-4332.00006</subfield><subfield code="q">text/html</subfield><subfield code="x">Verlag</subfield><subfield code="z">Deutschlandweit zugänglich</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-DJB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">20</subfield><subfield code="j">2000</subfield><subfield code="e">1</subfield><subfield code="h">0</subfield></datafield></record></collection>
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