  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.
