Dynamic hierarchical Dirichlet processes topic model using the power prior approach

• Published in: Journal of the Korean Statistical Society Vol. 50; no. 3; pp. 860 - 873
• Main Authors: Jeong, Kuhwan, Kim, Yongdai
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.09.2021; 한국통계학회
• Subjects: Applied Statistics; Bayesian Inference; Mathematics and Statistics; Review Article; Statistical Theory and Methods; Statistics; Statistics and Computing/Statistics Programs; 통계학; Power prior; Dynamic model; Topic model; Hierarchical Dirichlet processes
• ISSN: 1226-3192; 2005-2863
• DOI: 10.1007/s42952-021-00129-1

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.