标题： 贝叶斯模型不确定性的简约之旅 显示英文标题

标题： A Parsimonious Tour of Bayesian Model Uncertainty

摘要： 现代统计软件和机器学习库正在实现半自动化的统计推断。 在这一背景下，尝试将许多模型拟合到手头的数据似乎变得越来越容易，从而逆转了费舍尔式的科学方法，即在确定科学假设（因此也是模型）之后收集数据。 统计学家的新的目标是帮助实践者在如此庞大且异质的模型族中进行选择，这一任务被称为模型选择。 贝叶斯范式提供了一种系统化的方法来评估这个问题。 这种方法由哈罗德·杰弗里斯在他的1935年著作《概率理论》中提出，在过去几十年中经历了显著的发展，带来了若干新的理论和方法上的进展。 本文调查的重点是这些最近的发展，试图从不同社区所做工作的角度出发，提供一个统一的视角。 特别是，我们关注贝叶斯模型选择和平均技术的非渐近的样本外性能，并与惩罚最大似然法建立联系。 我们还描述了对更广泛的概率框架的最新扩展，包括高维、不可识别或似然-free 模型。 显示英文摘要

摘要： Modern statistical software and machine learning libraries are enabling semi-automated statistical inference. Within this context, it appears easier and easier to try and fit many models to the data at hand, reversing thereby the Fisherian way of conducting science by collecting data after the scientific hypothesis (and hence the model) has been determined. The renewed goal of the statistician becomes to help the practitioner choose within such large and heterogeneous families of models, a task known as model selection. The Bayesian paradigm offers a systematized way of assessing this problem. This approach, launched by Harold Jeffreys in his 1935 book Theory of Probability, has witnessed a remarkable evolution in the last decades, that has brought about several new theoretical and methodological advances. Some of these recent developments are the focus of this survey, which tries to present a unifying perspective on work carried out by different communities. In particular, we focus on non-asymptotic out-of-sample performance of Bayesian model selection and averaging techniques, and draw connections with penalized maximum likelihood. We also describe recent extensions to wider classes of probabilistic frameworks including high-dimensional, unidentifiable, or likelihood-free models.