The hierarchical Dirichlet processes (HDP) topic model is a Bayesian nonparametric model that provides a flexible mixed-membership to documents through topic allocation to each word. In this paper, we consider dynamic HDP topic models, in which the generative model changes in time, and develop a novel algorithm to update the posterior distribution dynamically by combining the variational inference algorithm and the power prior approach. An important advantage of the proposed algorithm is that it updates the posterior distribution by reusing a given batch algorithm without specifying a complicated dynamic generative model. Thus the proposed algorithm is conceptually and computationally simpler. By analyzing real datasets, we show that the proposed algorithm is a useful alternative approach to dynamic HDP topic identification.

分层Dirichlet流程（HDP）主题模型是贝叶斯非参数模型，该模型通过对每个单词的主题分配为文档提供了灵活的混合成员资格。在本文中，我们考虑了动态HDP主题模型，其中生成模型随时间变化，并且通过结合变分推理算法和幂先验方法，开发了一种动态更新后验分布的新算法。所提出算法的一个重要优点是，它可以通过重用给定的批处理算法来更新后验分布，而无需指定复杂的动态生成模型。因此，所提出的算法在概念上和计算上更简单。通过分析实际数据集，我们表明该算法是动态HDP主题识别的一种有用的替代方法。