Project management under uncertainty beyond beta : the generalized bicubic distribution
The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is ass...
Ausführliche Beschreibung
Autor*in: |
García Peréz, José Ignacio [verfasserIn] López Martín, María del Mar [verfasserIn] García García, Catalina [verfasserIn] Sánchez Granero, Miguel Ángel [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2016 |
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Übergeordnetes Werk: |
Enthalten in: Operations research perspectives - Amsterdam [u.a.] : Elsevier, 2014, 3(2016), Seite 67-76 |
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Übergeordnetes Werk: |
volume:3 ; year:2016 ; pages:67-76 |
Links: |
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DOI / URN: |
10.1016/j.orp.2016.09.001 |
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Katalog-ID: |
1019501820 |
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10.1016/j.orp.2016.09.001 doi 10419/178270 hdl (DE-627)1019501820 (DE-599)GBV1019501820 DE-627 ger DE-627 rda eng García Peréz, José Ignacio verfasserin (DE-588)171340957 (DE-627)06153174X (DE-576)132142252 aut Project management under uncertainty beyond beta the generalized bicubic distribution José García Pérez, María del Mar López Martín, Catalina García García, Miguel Ángel Sánchez Granero 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is assumed to be the underlying distribution has been widely debated. This paper presents the generalized bicubic distribution as a good alternative to the beta distribution since, when the variance depends on the mode, the generalized bicubic distribution approximates the kurtosis of the Gaussian distribution better than the beta distribution. In addition, this distribution presents good properties in the PERT methodology in relation to moderation and conservatism criteria. Two empirical applications are presented to demonstrate the adequateness of this new distribution. López Martín, María del Mar verfasserin (DE-588)1158691211 (DE-627)1020751452 (DE-576)504303198 aut García García, Catalina verfasserin aut Sánchez Granero, Miguel Ángel verfasserin aut Enthalten in Operations research perspectives Amsterdam [u.a.] : Elsevier, 2014 3(2016), Seite 67-76 Online-Ressource (DE-627)826105165 (DE-600)2821932-6 (DE-576)433076496 2214-7160 nnns volume:3 year:2016 pages:67-76 http://hdl.handle.net/10419/178270 Resolving-System kostenfrei Volltext https://doi.org/10.1016/j.orp.2016.09.001 Resolving-System kostenfrei Volltext https://www.sciencedirect.com/science/article/pii/S2214716016300252/pdfft?md5=baa3bd0ff53f1d679cd845cf40fb3ea5&pid=1-s2.0-S2214716016300252-main.pdf Verlag kostenfrei Volltext http://creativecommons.org/licenses/by-nc-nd/4.0/ Verlag Terms of use 46 GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2016 67-76 26 01 0206 1767049218 x1k 25-04-18 26 00 DE-206 56 Uncertainty 26 00 DE-206 56 PERT 26 00 DE-206 56 Bicubic distribution 26 00 DE-206 56 Beta distribution |
spelling |
10.1016/j.orp.2016.09.001 doi 10419/178270 hdl (DE-627)1019501820 (DE-599)GBV1019501820 DE-627 ger DE-627 rda eng García Peréz, José Ignacio verfasserin (DE-588)171340957 (DE-627)06153174X (DE-576)132142252 aut Project management under uncertainty beyond beta the generalized bicubic distribution José García Pérez, María del Mar López Martín, Catalina García García, Miguel Ángel Sánchez Granero 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is assumed to be the underlying distribution has been widely debated. This paper presents the generalized bicubic distribution as a good alternative to the beta distribution since, when the variance depends on the mode, the generalized bicubic distribution approximates the kurtosis of the Gaussian distribution better than the beta distribution. In addition, this distribution presents good properties in the PERT methodology in relation to moderation and conservatism criteria. Two empirical applications are presented to demonstrate the adequateness of this new distribution. López Martín, María del Mar verfasserin (DE-588)1158691211 (DE-627)1020751452 (DE-576)504303198 aut García García, Catalina verfasserin aut Sánchez Granero, Miguel Ángel verfasserin aut Enthalten in Operations research perspectives Amsterdam [u.a.] : Elsevier, 2014 3(2016), Seite 67-76 Online-Ressource (DE-627)826105165 (DE-600)2821932-6 (DE-576)433076496 2214-7160 nnns volume:3 year:2016 pages:67-76 http://hdl.handle.net/10419/178270 Resolving-System kostenfrei Volltext https://doi.org/10.1016/j.orp.2016.09.001 Resolving-System kostenfrei Volltext https://www.sciencedirect.com/science/article/pii/S2214716016300252/pdfft?md5=baa3bd0ff53f1d679cd845cf40fb3ea5&pid=1-s2.0-S2214716016300252-main.pdf Verlag kostenfrei Volltext http://creativecommons.org/licenses/by-nc-nd/4.0/ Verlag Terms of use 46 GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2016 67-76 26 01 0206 1767049218 x1k 25-04-18 26 00 DE-206 56 Uncertainty 26 00 DE-206 56 PERT 26 00 DE-206 56 Bicubic distribution 26 00 DE-206 56 Beta distribution |
allfields_unstemmed |
10.1016/j.orp.2016.09.001 doi 10419/178270 hdl (DE-627)1019501820 (DE-599)GBV1019501820 DE-627 ger DE-627 rda eng García Peréz, José Ignacio verfasserin (DE-588)171340957 (DE-627)06153174X (DE-576)132142252 aut Project management under uncertainty beyond beta the generalized bicubic distribution José García Pérez, María del Mar López Martín, Catalina García García, Miguel Ángel Sánchez Granero 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is assumed to be the underlying distribution has been widely debated. This paper presents the generalized bicubic distribution as a good alternative to the beta distribution since, when the variance depends on the mode, the generalized bicubic distribution approximates the kurtosis of the Gaussian distribution better than the beta distribution. In addition, this distribution presents good properties in the PERT methodology in relation to moderation and conservatism criteria. Two empirical applications are presented to demonstrate the adequateness of this new distribution. López Martín, María del Mar verfasserin (DE-588)1158691211 (DE-627)1020751452 (DE-576)504303198 aut García García, Catalina verfasserin aut Sánchez Granero, Miguel Ángel verfasserin aut Enthalten in Operations research perspectives Amsterdam [u.a.] : Elsevier, 2014 3(2016), Seite 67-76 Online-Ressource (DE-627)826105165 (DE-600)2821932-6 (DE-576)433076496 2214-7160 nnns volume:3 year:2016 pages:67-76 http://hdl.handle.net/10419/178270 Resolving-System kostenfrei Volltext https://doi.org/10.1016/j.orp.2016.09.001 Resolving-System kostenfrei Volltext https://www.sciencedirect.com/science/article/pii/S2214716016300252/pdfft?md5=baa3bd0ff53f1d679cd845cf40fb3ea5&pid=1-s2.0-S2214716016300252-main.pdf Verlag kostenfrei Volltext http://creativecommons.org/licenses/by-nc-nd/4.0/ Verlag Terms of use 46 GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2016 67-76 26 01 0206 1767049218 x1k 25-04-18 26 00 DE-206 56 Uncertainty 26 00 DE-206 56 PERT 26 00 DE-206 56 Bicubic distribution 26 00 DE-206 56 Beta distribution |
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10.1016/j.orp.2016.09.001 doi 10419/178270 hdl (DE-627)1019501820 (DE-599)GBV1019501820 DE-627 ger DE-627 rda eng García Peréz, José Ignacio verfasserin (DE-588)171340957 (DE-627)06153174X (DE-576)132142252 aut Project management under uncertainty beyond beta the generalized bicubic distribution José García Pérez, María del Mar López Martín, Catalina García García, Miguel Ángel Sánchez Granero 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is assumed to be the underlying distribution has been widely debated. This paper presents the generalized bicubic distribution as a good alternative to the beta distribution since, when the variance depends on the mode, the generalized bicubic distribution approximates the kurtosis of the Gaussian distribution better than the beta distribution. In addition, this distribution presents good properties in the PERT methodology in relation to moderation and conservatism criteria. Two empirical applications are presented to demonstrate the adequateness of this new distribution. López Martín, María del Mar verfasserin (DE-588)1158691211 (DE-627)1020751452 (DE-576)504303198 aut García García, Catalina verfasserin aut Sánchez Granero, Miguel Ángel verfasserin aut Enthalten in Operations research perspectives Amsterdam [u.a.] : Elsevier, 2014 3(2016), Seite 67-76 Online-Ressource (DE-627)826105165 (DE-600)2821932-6 (DE-576)433076496 2214-7160 nnns volume:3 year:2016 pages:67-76 http://hdl.handle.net/10419/178270 Resolving-System kostenfrei Volltext https://doi.org/10.1016/j.orp.2016.09.001 Resolving-System kostenfrei Volltext https://www.sciencedirect.com/science/article/pii/S2214716016300252/pdfft?md5=baa3bd0ff53f1d679cd845cf40fb3ea5&pid=1-s2.0-S2214716016300252-main.pdf Verlag kostenfrei Volltext http://creativecommons.org/licenses/by-nc-nd/4.0/ Verlag Terms of use 46 GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2016 67-76 26 01 0206 1767049218 x1k 25-04-18 26 00 DE-206 56 Uncertainty 26 00 DE-206 56 PERT 26 00 DE-206 56 Bicubic distribution 26 00 DE-206 56 Beta distribution |
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García Peréz, José Ignacio |
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García Peréz, José Ignacio 26 Uncertainty 26 PERT 26 Bicubic distribution 26 Beta distribution Project management under uncertainty beyond beta the generalized bicubic distribution |
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26 00 DE-206 56 Uncertainty 26 00 DE-206 56 PERT 26 00 DE-206 56 Bicubic distribution 26 00 DE-206 56 Beta distribution Project management under uncertainty beyond beta the generalized bicubic distribution José García Pérez, María del Mar López Martín, Catalina García García, Miguel Ángel Sánchez Granero |
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Project management under uncertainty beyond beta the generalized bicubic distribution |
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Project management under uncertainty beyond beta the generalized bicubic distribution José García Pérez, María del Mar López Martín, Catalina García García, Miguel Ángel Sánchez Granero |
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Project management under uncertainty beyond beta the generalized bicubic distribution |
abstract |
The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is assumed to be the underlying distribution has been widely debated. This paper presents the generalized bicubic distribution as a good alternative to the beta distribution since, when the variance depends on the mode, the generalized bicubic distribution approximates the kurtosis of the Gaussian distribution better than the beta distribution. In addition, this distribution presents good properties in the PERT methodology in relation to moderation and conservatism criteria. Two empirical applications are presented to demonstrate the adequateness of this new distribution. |
abstractGer |
The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is assumed to be the underlying distribution has been widely debated. This paper presents the generalized bicubic distribution as a good alternative to the beta distribution since, when the variance depends on the mode, the generalized bicubic distribution approximates the kurtosis of the Gaussian distribution better than the beta distribution. In addition, this distribution presents good properties in the PERT methodology in relation to moderation and conservatism criteria. Two empirical applications are presented to demonstrate the adequateness of this new distribution. |
abstract_unstemmed |
The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is assumed to be the underlying distribution has been widely debated. This paper presents the generalized bicubic distribution as a good alternative to the beta distribution since, when the variance depends on the mode, the generalized bicubic distribution approximates the kurtosis of the Gaussian distribution better than the beta distribution. In addition, this distribution presents good properties in the PERT methodology in relation to moderation and conservatism criteria. Two empirical applications are presented to demonstrate the adequateness of this new distribution. |
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Project management under uncertainty beyond beta |
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