GPU accelerated MCMC for modeling terrorist activity

White, Gentry and Porter, Michael D. (2014) GPU accelerated MCMC for modeling terrorist activity. Computational Statistics and Data Analysis, 71 643-651. doi:10.1016/j.csda.2013.03.027


Author White, Gentry
Porter, Michael D.
Title GPU accelerated MCMC for modeling terrorist activity
Journal name Computational Statistics and Data Analysis   Check publisher's open access policy
ISSN 0167-9473
1872-7352
Publication date 2014-03-01
Year available 2013
Sub-type Article (original research)
DOI 10.1016/j.csda.2013.03.027
Volume 71
Start page 643
End page 651
Total pages 9
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Abstract 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.
Keyword Bayesian
Convolution process
GPU parallel processing
Hawkes
MCMC
Terrorism
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Institute for Social Science Research - Publications
Official 2014 Collection
 
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