Publication: Research - peer-review › Article in proceedings – Annual report year: 2015
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.
|Title of host publication||Proceedings of the 23rd European Signal Processing Conference (EUSIPCO 2015)|
|State||Published - 2015|
|Event||23rd European Signal Processing Conference (EUSIPCO 2015) - Nice, France|
|Conference||23rd European Signal Processing Conference (EUSIPCO 2015)|
|Period||31/08/2015 → 04/09/2015|
|Name||Proceedings of the European Signal Processing Conference (EUSIPCO)|
|Citations||Web of Science® Times Cited: No match on DOI|
- Nonparametric Bayesian modeling, Infinite Relational Model, Numerical approximation
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