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dc.contributor.authorCooke, Roger M
dc.contributor.authorNauta, Maarten
dc.contributor.authorHavelaar, Arie H
dc.contributor.authorFels, Ine van der
dc.date.accessioned2007-01-03T09:10:12Z
dc.date.available2007-01-03T09:10:12Z
dc.date.issued2006-01-25
dc.identifier.citationReliability Engineering & System Safety Volume 91, Issues 10-11 , October-November 2006, Pages 1364-1372en
dc.identifier.doi10.1016/j.ress.2005.11.054
dc.identifier.urihttp://hdl.handle.net/10029/6757
dc.description.abstractWe discuss an application of probabilistic inversion techniques to a model of campylobacter transmission in chicken processing lines. Such techniques are indicated when we wish to quantify a model which is new and perhaps unfamiliar to the expert community. In this case there are no measurements for estimating model parameters, and experts are typically unable to give a considered judgment. In such cases, experts are asked to quantify their uncertainty regarding variables which can be predicted by the model. The experts’ distributions (after combination) are then pulled back onto the parameter space of the model, a process termed “probabilistic inversion”. This study illustrates two such techniques, iterative proportional fitting (IPF) and PARmeter fitting for uncertain models (PARFUM). In addition, we illustrate how expert judgement on predicted observable quantities in combination with probabilistic inversion may be used for model validation and/or model criticism.
dc.format.extent447871 bytes
dc.format.mimetypeapplication/pdf
dc.language.ison/aen
dc.publisherElsevieren
dc.subjectProbabilistic inversionen
dc.subjectIPFen
dc.subjectPARFUMen
dc.subjectCampylobacteren
dc.subjectTransport modelsen
dc.subjectExpert judgmenten
dc.subjectEntropyen
dc.subjectInformationen
dc.titleProbabilistic inversion for chicken processing linesen
dc.typeArticleen
dc.format.digYES
refterms.dateFOA2018-12-18T14:43:29Z
html.description.abstractWe discuss an application of probabilistic inversion techniques to a model of campylobacter transmission in chicken processing lines. Such techniques are indicated when we wish to quantify a model which is new and perhaps unfamiliar to the expert community. In this case there are no measurements for estimating model parameters, and experts are typically unable to give a considered judgment. In such cases, experts are asked to quantify their uncertainty regarding variables which can be predicted by the model. The experts’ distributions (after combination) are then pulled back onto the parameter space of the model, a process termed “probabilistic inversion”. This study illustrates two such techniques, iterative proportional fitting (IPF) and PARmeter fitting for uncertain models (PARFUM). In addition, we illustrate how expert judgement on predicted observable quantities in combination with probabilistic inversion may be used for model validation and/or model criticism.


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