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Poisson random fields for dynamic feature models

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Perrone, Valerio, Jenkins, Paul, Spanò, Dario and Teh, Yee Whye (2018) Poisson random fields for dynamic feature models. Journal of Machine Learning Research, 18 (1). 4626-4670 .

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Official URL: https://dl.acm.org/citation.cfm?id=3208008

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Abstract

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.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science > Computer Science
Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH): Bayesian statistical decision theory, Markov processes, Monte Carlo method
Journal or Publication Title: Journal of Machine Learning Research
Publisher: Journal of Machine Learning Research
ISSN: 1532-4435
Official Date: 31 January 2018
Dates:
DateEvent
31 January 2018Available
4 December 2017Accepted
Volume: 18
Number: 1
Page Range: 4626-4670
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
EP/L016710/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
EP/L018497/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
CRiSM[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
CRiSMHigher Education Funding Council for Englandhttp://dx.doi.org/10.13039/100011722
UNSPECIFIEDSeventh Framework Programmehttp://dx.doi.org/10.13039/100011102
617071H2020 European Research Councilhttp://dx.doi.org/10.13039/100010663
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