标题： 一类相关随机测度的统一表示 显示英文标题

标题： A unifying representation for a class of dependent random measures

摘要： 我们提出了一种基于对增广空间上的泊松过程进行删失的一般依赖随机测度构造方法。该框架不仅限于特定非参数模型的依赖版本，还可以应用于所有可以用完全随机测度表示的模型。几种现有的依赖随机测度可以被视为此框架的特例。推导了所得测度的一些有趣性质，并通过构建一个协变量相关的潜在特征模型和主题模型来展示框架的有效性，这些模型获得了更优的预测性能。 显示英文摘要

摘要： We present a general construction for dependent random measures based on thinning Poisson processes on an augmented space. The framework is not restricted to dependent versions of a specific nonparametric model, but can be applied to all models that can be represented using completely random measures. Several existing dependent random measures can be seen as specific cases of this framework. Interesting properties of the resulting measures are derived and the efficacy of the framework is demonstrated by constructing a covariate-dependent latent feature model and topic model that obtain superior predictive performance.