GPU accelerated MCMC for modeling terrorist activity

Gentry White and Michael D. Porter

Computational Statistics & Data Analysis, 2014, vol. 71, issue C, 643-651

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.

Keywords: GPU parallel processing; Bayesian; Hawkes; Convolution process; MCMC; Terrorism (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:71:y:2014:i:c:p:643-651

DOI: 10.1016/j.csda.2013.03.027

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