Do Bayesian Neural Networks Improve Weapon System Predictive Maintenance?
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Accesso online: | |
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| _version_ | 1866913187614425088 |
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| author | Potter, Michael Jun, Miru |
| author_facet | Potter, Michael Jun, Miru |
| contents | We implement a Bayesian inference process for Neural Networks to model the time to failure of highly reliable weapon systems with interval-censored data and time-varying covariates. We analyze and benchmark our approach, LaplaceNN, on synthetic and real datasets with standard classification metrics such as Receiver Operating Characteristic (ROC) Area Under Curve (AUC) Precision-Recall (PR) AUC, and reliability curve visualizations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_10494 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Do Bayesian Neural Networks Improve Weapon System Predictive Maintenance? Potter, Michael Jun, Miru Machine Learning Applications We implement a Bayesian inference process for Neural Networks to model the time to failure of highly reliable weapon systems with interval-censored data and time-varying covariates. We analyze and benchmark our approach, LaplaceNN, on synthetic and real datasets with standard classification metrics such as Receiver Operating Characteristic (ROC) Area Under Curve (AUC) Precision-Recall (PR) AUC, and reliability curve visualizations. |
| title | Do Bayesian Neural Networks Improve Weapon System Predictive Maintenance? |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2312.10494 |