Observation Value Analysis – Integral Part of Bayesian Diagnostics
The decision making process, in general, is understood as a process of selecting one of the available solutions to the problem. One of possible approaches supporting the process is Bayesian statistical decision theory providing a mathematical model to make decisions of a technical nature in conditio...
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Published in | Procedia engineering Vol. 123; pp. 24 - 31 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
2015
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Online Access | Get full text |
ISSN | 1877-7058 1877-7058 |
DOI | 10.1016/j.proeng.2015.10.053 |
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Abstract | The decision making process, in general, is understood as a process of selecting one of the available solutions to the problem. One of possible approaches supporting the process is Bayesian statistical decision theory providing a mathematical model to make decisions of a technical nature in conditions of uncertainty. Regarding above, a detailed subject of the research is to analyze the value of the observation, which is a part of preposterior analysis. For the presented network, the main objective was to determine, conducting of which of three tests is the most valuable from the perspective of determining possible need or possibility to omission expensive technical expertise. The basis of verification, which test is the most valuable, is therefore the comparison of expected value of sample information (EVSI) for each of the three tests.
The main advantage of preposterior analysis is answering the question which of the considered experiments is potentially the best source of information and what cost needs to be incurred on its execution (price of new information). If the cost of such an examination is relatively small compared to the value of information on the state of nature, this implies a direct recommendation to conduct the experiment.
In conclusion, the construction of the decision-making model being a reflection of the state of nature allows to determine the ranking of decisions, including those regarding selection of the optimal test. It is noteworthy, that test result itself can contribute to an increase in the expected value of the decision involved, but on the other hand, the reduction of uncertainty may be considerably outweighed to the necessity to incur expenses for this examination. |
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AbstractList | The decision making process, in general, is understood as a process of selecting one of the available solutions to the problem. One of possible approaches supporting the process is Bayesian statistical decision theory providing a mathematical model to make decisions of a technical nature in conditions of uncertainty. Regarding above, a detailed subject of the research is to analyze the value of the observation, which is a part of preposterior analysis. For the presented network, the main objective was to determine, conducting of which of three tests is the most valuable from the perspective of determining possible need or possibility to omission expensive technical expertise. The basis of verification, which test is the most valuable, is therefore the comparison of expected value of sample information (EVSI) for each of the three tests.
The main advantage of preposterior analysis is answering the question which of the considered experiments is potentially the best source of information and what cost needs to be incurred on its execution (price of new information). If the cost of such an examination is relatively small compared to the value of information on the state of nature, this implies a direct recommendation to conduct the experiment.
In conclusion, the construction of the decision-making model being a reflection of the state of nature allows to determine the ranking of decisions, including those regarding selection of the optimal test. It is noteworthy, that test result itself can contribute to an increase in the expected value of the decision involved, but on the other hand, the reduction of uncertainty may be considerably outweighed to the necessity to incur expenses for this examination. |
Author | Apollo, Magdalena Kembłowski, Marian W. |
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CitedBy_id | crossref_primary_10_1016_j_proeng_2017_07_195 crossref_primary_10_1088_1757_899X_245_8_082049 crossref_primary_10_3390_geosciences9080339 crossref_primary_10_1016_j_proeng_2017_08_046 crossref_primary_10_3390_sym13050744 |
Cites_doi | 10.1016/j.ress.2012.04.005 10.1016/j.ijproman.2014.01.001 10.1016/j.eswa.2009.02.019 10.2478/v10060-011-0059-8 10.1016/j.ijar.2015.03.005 |
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Keywords | decision theory Bayesian networks preposterior analysis expected value |
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References_xml | – reference: Fang Ch., Marle F., Zio E., Bocquet J.C.: Network theory-based analysis of risk interactions in large engineering projects. Reliability Engineering and System Safety 106/2012, page 1-10. – reference: Kemblowski M, Johnson P.C.: Environmental Monitoring, Modeling, And Management And Bayesian Belief Networks. Environmental Studies 2000, p. 133-142. – reference: Woudenberg S.P.D., van der Gaag L.C.: Propagation effects of model-calculated probability values in Bayesian networks. International Journal of Approximate Reasoning, Volume 61, June 2015, p. 1-15. – reference: Benjamin J.R., Cornell C.A.: Rachunek prawdopodobieństwa, statystyka matematyczna i teoria decyzji dla inżynierów. Wydawnictwa Naukowo-Techniczne. Warsaw, 1977. – reference: Chin K.S., Tang D.W., Yang J.B., Wang Sh.Y., Wang H.: Assessing new product development project risk by Bayesian network. Expert Systems with Applications 36 (6)/2009, s. 9879-9890. – reference: Kjaerulff U. B., Madsen A.L.: Bayesian Networks and Influence Diagrams. A Guide to Construction and Analysis. Springer Science+Business Media, LLC, 2008. – reference: Apollo M., Miszewska-Urbańska E.: Podejmowanie decyzji w warunkach niepewności przy użyciu sieci Bayesa – przykład zastosowania. Logistyka nr 6/2014, p. 1496-1504. – reference: Apollo M, Urbańska-Galewska E.: Poziom ryzyka inwestycyjnych działań budowlanych w projektach rewitalizacji. Inżynieria i Budownictwo, Zeszyt Gdański R. 70/nr 5, str. 263-265, Gdańsk, 2014. – reference: Fenton N., Neil M.: Risk assessment and decision analysis with bayesian networks. CRC Press, Taylor & Francis Group, LCC. ISBN: 978-1-4398-0910-5, 2013. – reference: Netica. Norsys Software Corporation 2014. http://www.norsys.com/tutorials/netica/secA/tut_A1.htm#WhatIsABayesNet. – reference: Khodakarami V., Abdi A.: Project cost risk analysis: A Bayesian networks approach formodeling dependencies between cost items. International Journal of Project Management 32 (7)/2014, p. 1233-1245. – reference: Mirosław-Świątek D., Kembłowski M., Jankowski W.: Application of the Bayesian Belief Nets in dam safety monitoring Annals of Warsaw University of Life Sciences-SGGW Land Reclamation. 44 (1)/2012, p. 45-55. – ident: 10.1016/j.proeng.2015.10.053_bib0035 – ident: 10.1016/j.proeng.2015.10.053_bib0025 doi: 10.1016/j.ress.2012.04.005 – ident: 10.1016/j.proeng.2015.10.053_bib0040 doi: 10.1016/j.ijproman.2014.01.001 – ident: 10.1016/j.proeng.2015.10.053_bib0010 – ident: 10.1016/j.proeng.2015.10.053_bib0020 doi: 10.1016/j.eswa.2009.02.019 – ident: 10.1016/j.proeng.2015.10.053_bib0005 – ident: 10.1016/j.proeng.2015.10.053_bib0030 – ident: 10.1016/j.proeng.2015.10.053_bib0015 – ident: 10.1016/j.proeng.2015.10.053_bib0045 – ident: 10.1016/j.proeng.2015.10.053_bib0055 – ident: 10.1016/j.proeng.2015.10.053_bib0050 doi: 10.2478/v10060-011-0059-8 – ident: 10.1016/j.proeng.2015.10.053_bib0060 doi: 10.1016/j.ijar.2015.03.005 |
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Title | Observation Value Analysis – Integral Part of Bayesian Diagnostics |
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