A Framework for Validation of Computer Models
We present a framework that enables computer model evaluation oriented toward answering the question: Does the computer model adequately represent reality? The proposed validation framework is a six-step procedure based on Bayesian and likelihood methodology. The Bayesian methodology is particularly...
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| Veröffentlicht in: | Technometrics 2007-05, Vol.49 (2), p.138-154 |
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| container_end_page | 154 |
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| container_issue | 2 |
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| container_title | Technometrics |
| container_volume | 49 |
| creator | Bayarri, Maria J Berger, James O Paulo, Rui Sacks, Jerry Cafeo, John A Cavendish, James Lin, Chin-Hsu Tu, Jian |
| description | We present a framework that enables computer model evaluation oriented toward answering the question: Does the computer model adequately represent reality? The proposed validation framework is a six-step procedure based on Bayesian and likelihood methodology. The Bayesian methodology is particularly well suited to treating the major issues associated with the validation process: quantifying multiple sources of error and uncertainty in computer models, combining multiple sources of information, and updating validation assessments as new information is acquired. Moreover, it allows inferential statements to be made about predictive error associated with model predictions in untested situations. The framework is implemented in a test bed example of resistance spot welding, to provide context for each of the six steps in the proposed validation process. |
| doi_str_mv | 10.1198/004017007000000092 |
| format | Article |
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The proposed validation framework is a six-step procedure based on Bayesian and likelihood methodology. The Bayesian methodology is particularly well suited to treating the major issues associated with the validation process: quantifying multiple sources of error and uncertainty in computer models, combining multiple sources of information, and updating validation assessments as new information is acquired. Moreover, it allows inferential statements to be made about predictive error associated with model predictions in untested situations. 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Data processing ; Model discrepancy ; Modeling ; Motivation ; Parametric models ; Prediction ; Probability and statistics ; Sciences and techniques of general use ; Software ; Statistics ; Studies ; Validity ; Welding</subject><ispartof>Technometrics, 2007-05, Vol.49 (2), p.138-154</ispartof><rights>American Statistical Association and the American Society for Quality 2007</rights><rights>Copyright 2007 The American Statistical Association and The American Society for Quality</rights><rights>2007 INIST-CNRS</rights><rights>Copyright American Society for Quality May 2007</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c446t-320183780b6a6e7b5b68436de4d425183064a84d3911217b2279c0af609bffd63</citedby><cites>FETCH-LOGICAL-c446t-320183780b6a6e7b5b68436de4d425183064a84d3911217b2279c0af609bffd63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/25471307$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/25471307$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,799,828,27901,27902,57992,57996,58225,58229</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18739324$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Bayarri, Maria J</creatorcontrib><creatorcontrib>Berger, James O</creatorcontrib><creatorcontrib>Paulo, Rui</creatorcontrib><creatorcontrib>Sacks, Jerry</creatorcontrib><creatorcontrib>Cafeo, John A</creatorcontrib><creatorcontrib>Cavendish, James</creatorcontrib><creatorcontrib>Lin, Chin-Hsu</creatorcontrib><creatorcontrib>Tu, Jian</creatorcontrib><title>A Framework for Validation of Computer Models</title><title>Technometrics</title><description>We present a framework that enables computer model evaluation oriented toward answering the question: Does the computer model adequately represent reality? The proposed validation framework is a six-step procedure based on Bayesian and likelihood methodology. The Bayesian methodology is particularly well suited to treating the major issues associated with the validation process: quantifying multiple sources of error and uncertainty in computer models, combining multiple sources of information, and updating validation assessments as new information is acquired. Moreover, it allows inferential statements to be made about predictive error associated with model predictions in untested situations. 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Data processing</subject><subject>Model discrepancy</subject><subject>Modeling</subject><subject>Motivation</subject><subject>Parametric models</subject><subject>Prediction</subject><subject>Probability and statistics</subject><subject>Sciences and techniques of general use</subject><subject>Software</subject><subject>Statistics</subject><subject>Studies</subject><subject>Validity</subject><subject>Welding</subject><issn>0040-1706</issn><issn>1537-2723</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kE9LxDAQxYMouK5-AUEogsfqTJIm6cHDsrgqrHhRryVtE-jaNmvSRfbbm7X-OQgOA3N4v3kzPEJOES4Rc3UFwAElQOzPyukemWDGZEolZftksgPSSIhDchTCCgAZVXJC0lmy8Loz786_Jtb55EW3Ta2HxvWJs8ncdevNYHzy4GrThmNyYHUbzMnXnJLnxc3T_C5dPt7ez2fLtOJcDCmjgIpJBaXQwsgyK4XiTNSG15xmUQLBteI1yxEpypJSmVegrYC8tLYWbErOR9-1d28bE4Zi5Ta-jycLikxIRKYiREeo8i4Eb2yx9k2n_bZAKHapFH9TiUsXX846VLq1XvdVE343lWQ5ozxyZyO3CoPzPzrNuEQGMurXo970MbVOx_zauhj0tnX-25T988cHlWN6PA</recordid><startdate>20070501</startdate><enddate>20070501</enddate><creator>Bayarri, Maria J</creator><creator>Berger, James O</creator><creator>Paulo, Rui</creator><creator>Sacks, Jerry</creator><creator>Cafeo, John A</creator><creator>Cavendish, James</creator><creator>Lin, Chin-Hsu</creator><creator>Tu, Jian</creator><general>Taylor & Francis</general><general>The American Society for Quality and The American Statistical Association</general><general>American Society for Quality Control</general><general>American Statistical Association</general><general>American Society for Quality</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8AO</scope><scope>8C1</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>L.-</scope><scope>L6V</scope><scope>M0C</scope><scope>M2P</scope><scope>M7S</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYYUZ</scope><scope>Q9U</scope><scope>S0X</scope></search><sort><creationdate>20070501</creationdate><title>A Framework for Validation of Computer Models</title><author>Bayarri, Maria J ; Berger, James O ; Paulo, Rui ; Sacks, Jerry ; Cafeo, John A ; Cavendish, James ; Lin, Chin-Hsu ; Tu, Jian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c446t-320183780b6a6e7b5b68436de4d425183064a84d3911217b2279c0af609bffd63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Accuracy</topic><topic>Applied sciences</topic><topic>Approximation</topic><topic>Bayesian analysis</topic><topic>Bias</topic><topic>Calibration</topic><topic>Computer analysis</topic><topic>Computer based modeling</topic><topic>Computer modeling</topic><topic>Computer science; control theory; systems</topic><topic>Data models</topic><topic>Data processing. 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Data processing</topic><topic>Model discrepancy</topic><topic>Modeling</topic><topic>Motivation</topic><topic>Parametric models</topic><topic>Prediction</topic><topic>Probability and statistics</topic><topic>Sciences and techniques of general use</topic><topic>Software</topic><topic>Statistics</topic><topic>Studies</topic><topic>Validity</topic><topic>Welding</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bayarri, Maria J</creatorcontrib><creatorcontrib>Berger, James O</creatorcontrib><creatorcontrib>Paulo, Rui</creatorcontrib><creatorcontrib>Sacks, Jerry</creatorcontrib><creatorcontrib>Cafeo, John A</creatorcontrib><creatorcontrib>Cavendish, James</creatorcontrib><creatorcontrib>Lin, Chin-Hsu</creatorcontrib><creatorcontrib>Tu, Jian</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>ABI/INFORM Global</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><jtitle>Technometrics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bayarri, Maria J</au><au>Berger, James O</au><au>Paulo, Rui</au><au>Sacks, Jerry</au><au>Cafeo, John A</au><au>Cavendish, James</au><au>Lin, Chin-Hsu</au><au>Tu, Jian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Framework for Validation of Computer Models</atitle><jtitle>Technometrics</jtitle><date>2007-05-01</date><risdate>2007</risdate><volume>49</volume><issue>2</issue><spage>138</spage><epage>154</epage><pages>138-154</pages><issn>0040-1706</issn><eissn>1537-2723</eissn><coden>TCMTA2</coden><abstract>We present a framework that enables computer model evaluation oriented toward answering the question: Does the computer model adequately represent reality? 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| ispartof | Technometrics, 2007-05, Vol.49 (2), p.138-154 |
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| source | Jstor Complete Legacy; JSTOR Mathematics & Statistics |
| subjects | Accuracy Applied sciences Approximation Bayesian analysis Bias Calibration Computer analysis Computer based modeling Computer modeling Computer science control theory systems Data models Data processing. List processing. Character string processing Exact sciences and technology Identifiability Mathematics Maximum likelihood estimation Memory organisation. Data processing Model discrepancy Modeling Motivation Parametric models Prediction Probability and statistics Sciences and techniques of general use Software Statistics Studies Validity Welding |
| title | A Framework for Validation of Computer Models |
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