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We implement a Bayesian inference process for Neural Networks to model thetime to failure of highly reliable weapon systems with interval-censored dataand time-varying covariates. We analyze and benchmark our approach, LaplaceNN,on synthetic and real datasets with standard classification metrics such asReceiver Operating Characteristic (ROC) Area Under Curve (AUC) Precision-Recall(PR) AUC, and reliability curve visualizations.

Do Bayesian Neural Networks Improve Weapon System Predictive Maintenance?