{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/35113"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/35113","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Failure record discounting in Bayesian analysis in Probabilistic Risk Assessment (PRA) : a space system application","abstract":"In estimating a system-specific binomial probability of failure on demand in Probabilistic Risk Assessment (PRA), the corresponding number of observed failures may be not directly applicable due to design or procedure changes that have been implemented in the system as a result of past failures. A methodology has been developed by NASA to account for partial applicability of past failures in Bayesian analysis by discounting the failure records. A series of sensitivity analyses on a specific case study showed that failure record discounting may result in failure distributions that are both optimistic and narrow. An alternative approach, which builds upon NASA's method, is proposed. This method combines an optimistic interpretation of the data, obtained with failure record discounting, with a pessimistic one, obtained with standard Bayesian updating without discounting, in a linear pooling fashion. The interpretation of the results in the proposed approach is done in such way that it displays the epistemic uncertainties that are inherent in the data and provides a better basis for the decision maker to make a decision based on his / her risk attitude. A comparison of the two methods is made based on the case study.","abstract_html":"In estimating a system-specific binomial probability of failure on demand in Probabilistic Risk Assessment (PRA), the corresponding number of observed failures may be not directly applicable due to design or procedure changes that have been implemented in the system as a result of past failures. A methodology has been developed by NASA to account for partial applicability of past failures in Bayesian analysis by discounting the failure records. A series of sensitivity analyses on a specific case study showed that failure record discounting may result in failure distributions that are both optimistic and narrow. An alternative approach, which builds upon NASA's method, is proposed. This method combines an optimistic interpretation of the data, obtained with failure record discounting, with a pessimistic one, obtained with standard Bayesian updating without discounting, in a linear pooling fashion. The interpretation of the results in the proposed approach is done in such way that it displays the epistemic uncertainties that are inherent in the data and provides a better basis for the decision maker to make a decision based on his / her risk attitude. A comparison of the two methods is made based on the case study.","abstract_has_math":false,"creators":["Lekkakos, Spyridon-Damianos"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"System Design and Management Program.","school":null,"contributors":[],"advisors":["George E. Apostolakis."],"committee_chairs":[],"committee_members":[],"year":2006,"date_issued":"2006","date_published":"2006","updated_at":"2026-07-22T22:22:01Z","subjects":["System Design and Management Program."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. 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A series of sensitivity analyses on a specific case study showed that failure record discounting may result in failure distributions that are both optimistic and narrow. An alternative approach, which builds upon NASA's method, is proposed. This method combines an optimistic interpretation of the data, obtained with failure record discounting, with a pessimistic one, obtained with standard Bayesian updating without discounting, in a linear pooling fashion. The interpretation of the results in the proposed approach is done in such way that it displays the epistemic uncertainties that are inherent in the data and provides a better basis for the decision maker to make a decision based on his / her risk attitude. 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