The Library
Poisson random fields for dynamic feature models
Tools
Tools
Tools
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 .
|
PDF
WRAP-poisson-random-fields-dynamic-feature-models-Perrone-2017.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1924Kb) | Preview |
|
![[img]](http://wrap.warwick.ac.uk/style/images/fileicons/application_pdf.png) |
PDF
WRAP-poisson-random-fields-dynamic-feature-models-Perrone-2017.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (1517Kb) |
|
|
PDF
SHERPA-Journal of Machine Learning Research-01-05-2019.pdf - Permissions Correspondence Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (104Kb) |
Official URL: https://dl.acm.org/citation.cfm?id=3208008
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: |
|
|||||||||||||||||||||
| 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: |
|
|||||||||||||||||||||
| Related URLs: | ||||||||||||||||||||||
| Open Access Version: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
