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.

翻译：我们为神经网络实现了一种贝叶斯推理过程，用于对具有区间删失数据和时变协变量的高可靠武器系统的故障时间进行建模。我们在合成数据集和真实数据集上分析了我们的方法LaplaceNN，并使用标准分类指标（如接收者操作特征曲线下面积（ROC AUC）、精确率-召回率曲线下面积（PR AUC）以及可靠性曲线可视化）进行了基准测试。