Bayes assisted model assessment

数据分析的统计方法依赖于概率模型，它描述了产生数据的机制的假设性质。所做的假设有时对分析的有效性至关重要。因此，统计学家开发了统计方法来检查所做的假设；由此产生的工具被描述为模型评估、模型验证或拟合优度技术。目前使用的概率模型已经变得非常复杂，迫切需要更多这样的模型评估方法。同时，已经开发的方法通常是非常特别的——不基于任何总体原则。最后，在现代复杂模型中，模型评估方法的统计行为很难计算。

Statistical methods of data analysis rely on probabilistic models which describe the assumed nature of the mechanism which generated the data. The assumptions made are sometimes critical to the validity of the analysis. Statisticians have therefore developed statistical methods to check the assumptions being made; the resulting tools are described as model assessment, model validation or goodness-of-fit techniques. Probabilistic models in current use have become very complex and there is a great need for more such model assessment methods. At the same time the methods already developed are often quite ad hoc -- not based on any overarching principles. Finally the statistical behaviour of model assessment methods can be very hard to compute in modern complex models.