Discovering Dynamic Topic Transitions in Topic Models

Olga Isupova, Danil Kuzin, Lyudmila Mihaylova

Research output: Contribution to conferencePaperpeer-review


In this paper we propose a Dirichlet process topic model for representing patterns in complex dynamic data. We assume that in a dynamic corpus topics do not randomly appear and disappear but rather replace each other following transitional rules. We model topic transitions as a Markov chain with an infinite hidden Markov model. The proposed model is then formulated as a Dirichlet process with a hierarchical Dirichlet process hidden Markov model prior.
Original languageEnglish
Publication statusPublished - 2018
EventAll of Bayesian Nonparametrics (Especially the Useful Bits): A workshop at the Thirty-Second Annual Conference on Neural Information Processing Systems (NeurIPS 2018). - Palais des Congrès de Montréal, Montréal, Canada
Duration: 7 Dec 20187 Dec 2018


WorkshopAll of Bayesian Nonparametrics (Especially the Useful Bits)
Abbreviated titleBNP@NeurIPS 2018
Internet address


Dive into the research topics of 'Discovering Dynamic Topic Transitions in Topic Models'. Together they form a unique fingerprint.

Cite this