GPU accelerated MCMC for modelling terrorist activity

White, Gentry & Porter, Michael D. (2013) GPU accelerated MCMC for modelling terrorist activity. Computational Statistics and Data Analysis, 71, pp. 643-651.

View at publisher


The use of graphical processing unit (GPU) parallel processing is becoming a part of mainstream statistical practice. The reliance of Bayesian statistics on Markov Chain Monte Carlo (MCMC) methods makes the applicability of parallel processing not immediately obvious. It is illustrated that there are substantial gains in improved computational time for MCMC and other methods of evaluation by computing the likelihood using GPU parallel processing. Examples use data from the Global Terrorism Database to model terrorist activity in Colombia from 2000 through 2010 and a likelihood based on the explicit convolution of two negative-binomial processes. Results show decreases in computational time by a factor of over 200. Factors influencing these improvements and guidelines for programming parallel implementations of the likelihood are discussed.

Impact and interest:

2 citations in Scopus
Search Google Scholar™
2 citations in Web of Science®

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 68790
Item Type: Journal Article
Refereed: Yes
Keywords: GPU, MCMC, Terrorism, Self-exciting Model
DOI: 10.1016/j.csda.2013.03.027
ISSN: 0167-9473
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Divisions: Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Elsevier
Copyright Statement: This is the author’s version of a work that was accepted for publication in Computational Statistics and Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics and Data Analysis, [VOL 71, (2014)] DOI: 10.1016/j.csda.2013.03.027
Deposited On: 20 Mar 2014 00:02
Last Modified: 21 Mar 2014 00:58

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page