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GPU accelerated MCMC for modeling terrorist activity

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  • White, Gentry
  • Porter, Michael D.

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.

Suggested Citation

  • White, Gentry & Porter, Michael D., 2014. "GPU accelerated MCMC for modeling terrorist activity," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 643-651.
    • Handle: RePEc:eee:csdana:v:71:y:2014:i:c:p:643-651
    DOI: 10.1016/j.csda.2013.03.027
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    References listed on IDEAS

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    Cited by:

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    2. Michael Platzer & Thomas Reutterer, 2016. "Ticking Away the Moments: Timing Regularity Helps to Better Predict Customer Activity," Marketing Science, INFORMS, vol. 35(5), pages 779-799, September.

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