The paper "Predictive Uncertainty Estimation via Prior Networks" by Andrey Malinin and Mark Gales introduces a new framework called Prior Networks (PNs) for modeling predictive uncertainty in AI systems. The authors highlight the importance of distinguishing between different sources of uncertainty: model uncertainty, data uncertainty, and distributional uncertainty. PNs explicitly model distributional uncertainty by parameterizing a prior distribution over predictive distributions. The focus is on classification tasks, and the effectiveness of PNs is evaluated on the MNIST and CIFAR-10 datasets for identifying out-of-distribution (OOD) samples and detecting misclassification. Experiments show that PNs outperform previous methods in these tasks, demonstrating their ability to distinguish between data and distributional uncertainty. The paper also discusses various uncertainty metrics and their application to different sources of uncertainty, providing a comprehensive framework for understanding and estimating predictive uncertainty.The paper "Predictive Uncertainty Estimation via Prior Networks" by Andrey Malinin and Mark Gales introduces a new framework called Prior Networks (PNs) for modeling predictive uncertainty in AI systems. The authors highlight the importance of distinguishing between different sources of uncertainty: model uncertainty, data uncertainty, and distributional uncertainty. PNs explicitly model distributional uncertainty by parameterizing a prior distribution over predictive distributions. The focus is on classification tasks, and the effectiveness of PNs is evaluated on the MNIST and CIFAR-10 datasets for identifying out-of-distribution (OOD) samples and detecting misclassification. Experiments show that PNs outperform previous methods in these tasks, demonstrating their ability to distinguish between data and distributional uncertainty. The paper also discusses various uncertainty metrics and their application to different sources of uncertainty, providing a comprehensive framework for understanding and estimating predictive uncertainty.