Application of Bayesian Approach to Reduce the Uncertainty in Expert Judgments by Using a Posteriori Mean Function
Much 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, includin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/19/2455 |
| _version_ | 1797515997983277056 |
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| author | Irina Vinogradova-Zinkevič |
| author_facet | Irina Vinogradova-Zinkevič |
| author_sort | Irina Vinogradova-Zinkevič |
| collection | DOAJ |
| description | Much 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 <i>a posteriori</i> 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 <i>a priori</i> information and expert error. The results are illustrated by the example of the evaluation of distance learning courses. |
| first_indexed | 2024-03-10T06:55:06Z |
| format | Article |
| id | doaj.art-84ece6b0faf74aafba854aad27f2e758 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T06:55:06Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-84ece6b0faf74aafba854aad27f2e7582023-11-22T16:30:36ZengMDPI AGMathematics2227-73902021-10-01919245510.3390/math9192455Application of Bayesian Approach to Reduce the Uncertainty in Expert Judgments by Using a Posteriori Mean FunctionIrina Vinogradova-Zinkevič0Department of Information Technologies, Vilnius Gediminas Technical University, 10223 Vilnius, LithuaniaMuch 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 <i>a posteriori</i> 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 <i>a priori</i> information and expert error. The results are illustrated by the example of the evaluation of distance learning courses.https://www.mdpi.com/2227-7390/9/19/2455decision makingBayesian approachuncertaintyexpert judgmentssubjectivityprobability density functions |
| spellingShingle | Irina Vinogradova-Zinkevič Application of Bayesian Approach to Reduce the Uncertainty in Expert Judgments by Using a Posteriori Mean Function Mathematics decision making Bayesian approach uncertainty expert judgments subjectivity probability density functions |
| title | Application of Bayesian Approach to Reduce the Uncertainty in Expert Judgments by Using a Posteriori Mean Function |
| title_full | Application of Bayesian Approach to Reduce the Uncertainty in Expert Judgments by Using a Posteriori Mean Function |
| title_fullStr | Application of Bayesian Approach to Reduce the Uncertainty in Expert Judgments by Using a Posteriori Mean Function |
| title_full_unstemmed | Application of Bayesian Approach to Reduce the Uncertainty in Expert Judgments by Using a Posteriori Mean Function |
| title_short | Application of Bayesian Approach to Reduce the Uncertainty in Expert Judgments by Using a Posteriori Mean Function |
| title_sort | application of bayesian approach to reduce the uncertainty in expert judgments by using a posteriori mean function |
| topic | decision making Bayesian approach uncertainty expert judgments subjectivity probability density functions |
| url | https://www.mdpi.com/2227-7390/9/19/2455 |
| work_keys_str_mv | AT irinavinogradovazinkevic applicationofbayesianapproachtoreducetheuncertaintyinexpertjudgmentsbyusingaposteriorimeanfunction |