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

Mikkel Nørgaard Schmidt, Kristoffer Jon Albers

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

265 Downloads (Pure)


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)
Publication date2015
ISBN (Print)978-0-9928626-3-3
Publication statusPublished - 2015
Event23rd European Signal Processing Conference (EUSIPCO 2015) - Nice, France
Duration: 31 Aug 20154 Sep 2015
Conference number: 23


Conference23rd European Signal Processing Conference (EUSIPCO 2015)
Internet address
SeriesProceedings of the European Signal Processing Conference


  • Nonparametric Bayesian modeling
  • Infinite Relational Model
  • Numerical approximation

Fingerprint Dive into the research topics of 'Numerical approximations for speeding up mcmc inference in the infinite relational model'. Together they form a unique fingerprint.

Cite this