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 inProcedia engineering Vol. 123; pp. 24 - 31
Main Authors Apollo, Magdalena, Kembłowski, Marian W.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 2015
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ISSN1877-7058
1877-7058
DOI10.1016/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.
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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crossref_primary_10_3390_geosciences9080339
crossref_primary_10_1016_j_proeng_2017_08_046
crossref_primary_10_3390_sym13050744
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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.
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– reference: Netica. Norsys Software Corporation 2014. http://www.norsys.com/tutorials/netica/secA/tut_A1.htm#WhatIsABayesNet.
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– 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.
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Snippet 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...
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SubjectTerms Bayesian networks
decision theory
expected value
preposterior analysis
Title Observation Value Analysis – Integral Part of Bayesian Diagnostics
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Volume 123
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