Scalable and Robust Bayesian Inference for Implicit Statistical Models

该项目旨在开发下一代有效的方法，将复杂的基于模拟的统计模型拟合到数据中。从业人员和科学家对这种隐式模型感兴趣，以实现发现，产生准确的预测，并在不确定的情况下为决策提供信息。然而，相关的计算成本将研究人员限制在必须具有少量参数和良好指定的隐式模型中，阻碍了科学进步。该项目将为隐式模型开发新的计算方法和算法，这些隐式模型可扩展到高维，并且对错误规范具有鲁棒性。在流行病学、生物学、生态学和其他领域更常规地使用隐式模型将会带来好处。

This project aims to develop the next generation of efficient methods for fitting complex simulation-based statistical models to data. Practitioners and scientists are interested in such implicit models to enable discoveries, produce accurate predictions and inform decisions under uncertainty. However, the associated computational cost has restricted researchers to implicit models that must have a small number of parameters and be well specified, impeding scientific progress. This project will develop new computational methods and algorithms for implicit models that scale to high dimensions and are robust to misspecification. Benefits will arise from the more routine use of implicit models in epidemiology, biology, ecology and other fields.