Loss-based Bayesian Prediction

这个项目提出了一个新的预测范式。使用最先进的计算方法，该项目旨在产生准确的，符合目的的预测，通过设计，减少预测不准确时造成的损失。不依赖于正确统计模型知识的新预测方法的理论验证是预期的结果，对其在经验设置中的表现进行广泛的数值评估也是预期的结果。在所有因预测不准确而导致严重后果的领域，新的范式都将带来显著的好处。如果预测不正确，将特别注意导致重大经济、财政或环境损失的问题。

This project proposes a new paradigm for prediction. Using state-of-the-art computational methods, the project aims to produce accurate, fit for purpose, predictions which, by design, reduce the loss incurred when the prediction is inaccurate. Theoretical validation of the new predictive method, without reliance on knowledge of the correct statistical model, is an expected outcome, as is an extensive numerical assessment of its performance in empirical settings. The new paradigm should produce significant benefits for all fields in which the consequences of predictive inaccuracy are severe. Problems that lead to substantial economic, financial or environmental loss if predictions are incorrect will be given particular attention.