Numerical approximations for speeding up mcmc inference in the infinite relational model

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2015

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The infinite relational model (IRM) is a powerful model for discovering clusters in complex networks; however, the computational speed of Markov chain Monte Carlo inference in the model can be a limiting factor when analyzing large networks. We investigate how using numerical approximations of the log-Gamma function in evaluating the likelihood of the IRM can improve the computational speed of MCMC inference, and how it affects the performance of the model. Using an ensemble of networks generated from the IRM, we compare three approximations in terms of their generalization performance measured on test data. We demonstrate that the computational time for MCMC inference can be reduced by a factor of two without affecting the performance, making it worthwhile in practical situations when on a computational budget.
Original languageEnglish
Title of host publicationProceedings of the 23rd European Signal Processing Conference (EUSIPCO 2015)
PublisherIEEE
Publication date2015
Pages2781-2785
ISBN (print)978-0-9928626-3-3
DOIs
StatePublished - 2015
Event23rd European Signal Processing Conference (EUSIPCO 2015) - Nice, France

Conference

Conference23rd European Signal Processing Conference (EUSIPCO 2015)
Number23
CountryFrance
CityNice
Period31/08/201504/09/2015
Internet address
NameProceedings of the European Signal Processing Conference (EUSIPCO)
ISSN (Print)2076-1465
CitationsWeb of Science® Times Cited: No match on DOI

Keywords

  • Nonparametric Bayesian modeling, Infinite Relational Model, Numerical approximation
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