标题： 非交换先验的贝叶斯非参数模型调查 显示英文标题

标题： A survey of non-exchangeable priors for Bayesian nonparametric models

摘要： 依赖性非参数过程扩展了对测度的分布（例如狄利克雷过程和贝塔过程），以给出对测度集合的分布，通常这些测度由协变量空间中的值索引。 当可交换性假设不成立时，这类模型适合作为先验分布，并且我们希望模型能够随着一组协变量灵活变化。 自麦克伊琴 [1] 正式提出依赖性非参数过程的概念以来，在统计学和机器学习文献中已经提出了并使用了许多模型。 其中许多模型表现出潜在的相似性，我们希望通过理解这些相似性来帮助选择合适的先验分布、开发新模型以及利用推理技术。 显示英文摘要

摘要： Dependent nonparametric processes extend distributions over measures, such as the Dirichlet process and the beta process, to give distributions over collections of measures, typically indexed by values in some covariate space. Such models are appropriate priors when exchangeability assumptions do not hold, and instead we want our model to vary fluidly with some set of covariates. Since the concept of dependent nonparametric processes was formalized by MacEachern [1], there have been a number of models proposed and used in the statistics and machine learning literatures. Many of these models exhibit underlying similarities, an understanding of which, we hope, will help in selecting an appropriate prior, developing new models, and leveraging inference techniques.