Fast approximate inference methods: new algorithms, applications and theory

该项目旨在为快速近似推理开发新的算法和理论，并奠定基础设施，以帮助未来的扩展。快速近似推理方法是将大型复杂统计模型拟合到大数据集上的一种原则性强、可扩展的方法。在速度至关重要而传统方法不可行的应用中，它们发挥了自己的作用。该项目旨在从改善保险数据仓库的业务决策到改进医疗成像技术等方面取得实际成果。

This project aims to develop new algorithms and theory for fast approximate inference and lay down infrastructure to aid future extensions. Fast approximate inference methods are a principled and extensible means of fitting large and complex statistical models to big data sets. They come into their own in applications where speed is paramount and traditional approaches are not feasible. The project aims to lead to practical outcomes from better business decision-making for insurance data warehouses, to improved medical imaging technology.