标题： 相关随机测度 显示英文标题

标题： Correlated Random Measures

摘要： 我们开发了相关随机测度（其中原子权重可以表现出灵活的依赖模式），并用其构建了强大的分层贝叶斯非参数模型。 分层贝叶斯非参数模型通常由完全随机测度构建，这是基于泊松过程的一种构造方法，在这种构造中，原子权重是相互独立的。 完全随机测度在相应的分层模型中隐含了较强的独立性假设，并且这些假设在现实世界中往往是不合适的。 相关随机测度解决了这一局限性。 它们通过结合高斯过程和泊松过程来对测度内的相关性进行建模。 例如，借助相关随机测度，我们可以构建潜在特征模型，从而同时推断潜在特征的属性及其依赖模式。 我们也开发了几个其他例子。 我们研究了一种针对成对计数数据的相关随机测度模型。 我们推导出一种高效的变分推理算法，并在文档、网页点击和电子健康记录的大规模数据集上展示了改进的预测性能。 显示英文摘要

摘要： We develop correlated random measures, random measures where the atom weights can exhibit a flexible pattern of dependence, and use them to develop powerful hierarchical Bayesian nonparametric models. Hierarchical Bayesian nonparametric models are usually built from completely random measures, a Poisson-process based construction in which the atom weights are independent. Completely random measures imply strong independence assumptions in the corresponding hierarchical model, and these assumptions are often misplaced in real-world settings. Correlated random measures address this limitation. They model correlation within the measure by using a Gaussian process in concert with the Poisson process. With correlated random measures, for example, we can develop a latent feature model for which we can infer both the properties of the latent features and their dependency pattern. We develop several other examples as well. We study a correlated random measure model of pairwise count data. We derive an efficient variational inference algorithm and show improved predictive performance on large data sets of documents, web clicks, and electronic health records.