Poisson random fields for dynamic feature models

Journal article

We present the Wright-Fisher Indian buffet process (WF-IBP), a probabilistic model for time-dependent data assumed to have been generated by an unknown number of latent features. This model is suitable as a prior in Bayesian nonparametric feature allocation models in which the features underlying the observed data exhibit a dependency structure over time. More specifically, we establish a new framework for generating dependent Indian buffet processes, where the Poisson random field model from population genetics is used as a way of constructing dependent beta processes. Inference in the model is complex, and we describe a sophisticated Markov Chain Monte Carlo algorithm for exact posterior simulation. We apply our construction to develop a nonparametric focused topic model for collections of time-stamped text documents and test it on the full corpus of NIPS papers published from 1987 to 2015.

Authors: Perrone, V; Jenkins, P; Spano, D; Teh, Y

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Journal of Machine Learning Research
• Journal: Journal of Machine Learning Research
• Volume: 18
• Article number: 127
• Publication date: 2017-12-01
• Acceptance date: 2017-12-01
• EISSN: 1533-7928
• ISSN: 1532-4435
• Keywords: Monte Carlo; Markov chain; topic model; Poisson random field; Indian buffet process; Bayesian nonparametrics