Uncertainty quantification and robustness to distribution shifts are important goals in machine learning and artificial intelligence. Although Bayesian neural networks (BNNs) allow for uncertainty in the predictions to be assessed, different sources of uncertainty are indistinguishable. We present imprecise Bayesian neural networks (IBNNs); they generalize and overcome some of the drawbacks of standard BNNs. These latter are trained using a single prior and likelihood distributions, whereas IBNNs are trained using credal prior and likelihood sets. They allow to distinguish between aleatoric and epistemic uncertainties, and to quantify them. In addition, IBNNs are robust in the sense of Bayesian sensitivity analysis, and are more robust than BNNs to distribution shift. They can also be used to compute sets of outcomes that enjoy PAC-like properties. We apply IBNNs to two case studies. One, to model blood glucose and insulin dynamics for artificial pancreas control, and two, for motion prediction in autonomous driving scenarios. We show that IBNNs performs better when compared to an ensemble of BNNs benchmark.

翻译：不确定性量化与对分布偏移的鲁棒性是机器学习与人工智能领域的重要目标。尽管贝叶斯神经网络（BNNs）能够评估预测中的不确定性，但不同来源的不确定性难以区分。本文提出不精确贝叶斯神经网络（IBNNs），它推广并克服了标准BNNs的若干缺陷。标准BNNs使用单一先验分布和似然分布进行训练，而IBNNs则采用信度先验集与似然集进行训练。该方法能够区分偶然不确定性与认知不确定性并对其进行量化。此外，IBNNs在贝叶斯敏感性分析意义上具有鲁棒性，且相比BNNs对分布偏移表现出更强的鲁棒性。该方法还可用于计算具有PAC类性质的预测结果集。我们将IBNNs应用于两个案例研究：其一为人工胰腺控制中的血糖与胰岛素动力学建模，其二为自动驾驶场景中的运动预测。实验表明，与BNNs集成基准方法相比，IBNNs表现更优。