Do Bayesian Neural Networks Improve Weapon System Predictive Maintenance?
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 18 references that are in the map. First posted on arXiv 16 Dec 2023.
Builds on
10 of 18 references.
References · 10
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