Bayesian nonparametric system reliability using sets of priors
An imprecise Bayesian nonparametric approach to system reliability with multiple types of components is developed. This allows modelling partial or imperfect prior knowledge on component failure distributions in a flexible way through bounds on the functioning probability. Given component level te...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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Elsevier
2016
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| _version_ | 1797055612832448512 |
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| author | Walter, G Aslett, L Coolen, F |
| author_facet | Walter, G Aslett, L Coolen, F |
| author_sort | Walter, G |
| collection | OXFORD |
| description | An imprecise Bayesian nonparametric approach to system reliability with multiple types of components is developed. This allows modelling partial or imperfect prior knowledge on component failure distributions in a flexible way through bounds on the functioning probability. Given component level test data these bounds are propagated to bounds on the posterior predictive distribution for the functioning probability of a new system containing components exchangeable with those used in testing. The method further enables identification of prior-data conflict at the system level based on component level test data. New results on first-order stochastic dominance for the Beta-Binomial distribution make the technique computationally tractable. Our methodological contributions can be immediately used in applications by reliability practitioners as we provide easy to use software tools. |
| first_indexed | 2024-03-06T19:12:22Z |
| format | Journal article |
| id | oxford-uuid:17309046-8612-455b-b0c9-9f2dc3d365a5 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:12:22Z |
| publishDate | 2016 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:17309046-8612-455b-b0c9-9f2dc3d365a52022-03-26T10:35:48ZBayesian nonparametric system reliability using sets of priorsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:17309046-8612-455b-b0c9-9f2dc3d365a5EnglishSymplectic Elements at OxfordElsevier2016Walter, GAslett, LCoolen, FAn imprecise Bayesian nonparametric approach to system reliability with multiple types of components is developed. This allows modelling partial or imperfect prior knowledge on component failure distributions in a flexible way through bounds on the functioning probability. Given component level test data these bounds are propagated to bounds on the posterior predictive distribution for the functioning probability of a new system containing components exchangeable with those used in testing. The method further enables identification of prior-data conflict at the system level based on component level test data. New results on first-order stochastic dominance for the Beta-Binomial distribution make the technique computationally tractable. Our methodological contributions can be immediately used in applications by reliability practitioners as we provide easy to use software tools. |
| spellingShingle | Walter, G Aslett, L Coolen, F Bayesian nonparametric system reliability using sets of priors |
| title | Bayesian nonparametric system reliability using sets of priors |
| title_full | Bayesian nonparametric system reliability using sets of priors |
| title_fullStr | Bayesian nonparametric system reliability using sets of priors |
| title_full_unstemmed | Bayesian nonparametric system reliability using sets of priors |
| title_short | Bayesian nonparametric system reliability using sets of priors |
| title_sort | bayesian nonparametric system reliability using sets of priors |
| work_keys_str_mv | AT walterg bayesiannonparametricsystemreliabilityusingsetsofpriors AT aslettl bayesiannonparametricsystemreliabilityusingsetsofpriors AT coolenf bayesiannonparametricsystemreliabilityusingsetsofpriors |