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dc.contributor.authorVinogradova-Zinkevič, Irina
dc.date.accessioned2023-09-18T16:09:48Z
dc.date.available2023-09-18T16:09:48Z
dc.date.issued2021
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/111979
dc.description.abstractMuch applied research uses expert judgment as a primary or additional data source, thus the problem solved in this publication is relevant. Despite the expert’s experience and competence, the evaluation is subjective and has uncertainty in it. There are various reasons for this uncertainty, including the expert’s incomplete competence, the expert’s character and personal qualities, the expert’s attachment to the opinion of other experts, and the field of the task to be solved. This paper presents a new way to use the Bayesian method to reduce the uncertainty of an expert judgment by correcting the expert’s evaluation by the a posteriori mean function. The Bayesian method corrects the expert’s evaluation, taking into account the expert’s competence and accumulated long-term experience. Since the paper uses a continuous case of the Bayesian formula, perceived as a continuous approximation of experts’ evaluations, this is not only the novelty of this work, but also a new result in the theory of the Bayesian method and its application. The paper investigates various combinations of the probability density functions of a priori information and expert error. The results are illustrated by the example of the evaluation of distance learning courses.eng
dc.formatPDF
dc.format.extentp. 2455-2479
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.relation.isreferencedbyScopus
dc.relation.isreferencedbyDOAJ
dc.relation.isreferencedbyJ-Gate
dc.relation.isreferencedbyGale's Academic OneFile
dc.source.urihttps://doi.org/10.3390/math9192455
dc.titleApplication of Bayesian approach to reduce the uncertainty in expert judgments by using a posteriori mean function
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.accessRightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
dcterms.licenseCreative Commons – Attribution – 4.0 International
dcterms.references52
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyFundamentinių mokslų fakultetas / Faculty of Fundamental Sciences
dc.subject.researchfieldN 009 - Informatika / Computer science
dc.subject.researchfieldT 007 - Informatikos inžinerija / Informatics engineering
dc.subject.researchfieldN 001 - Matematika / Mathematics
dc.subject.studydirectionB01 - Informatika / Informatics
dc.subject.studydirectionB04 - Informatikos inžinerija / Informatics engineering
dc.subject.studydirectionA02 - Taikomoji matematika / Applied mathematics
dc.subject.vgtuprioritizedfieldsFM0101 - Fizinių, technologinių ir ekonominių procesų matematiniai modeliai / Mathematical models of physical, technological and economic processes
dc.subject.ltspecializationsL104 - Nauji gamybos procesai, medžiagos ir technologijos / New production processes, materials and technologies
dc.subject.endecision making
dc.subject.enBayesian approach
dc.subject.enuncertainty
dc.subject.enexpert judgments
dc.subject.ensubjectivity
dc.subject.enprobability density functions
dc.subject.enposteriori mean function
dcterms.sourcetitleMathematics: Special issue: Decision making and its applications
dc.description.issueiss. 19
dc.description.volumevol. 9
dc.publisher.nameMDPI
dc.publisher.cityBasel
dc.identifier.doi10.3390/math9192455
dc.identifier.elaba106884477


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