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Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach

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  • Strid, Ingvar

Abstract

Prefetching is a simple and general method for single-chain parallelisation of the Metropolis-Hastings algorithm based on the idea of evaluating the posterior in parallel and ahead of time. Improved Metropolis-Hastings prefetching algorithms are presented and evaluated. It is shown how to use available information to make better predictions of the future states of the chain and increase the efficiency of prefetching considerably. The optimal acceptance rate for the prefetching random walk Metropolis-Hastings algorithm is obtained for a special case and it is shown to decrease in the number of processors employed. The performance of the algorithms is illustrated using a well-known macroeconomic model. Bayesian estimation of DSGE models, linearly or nonlinearly approximated, is identified as a potential area of application for prefetching methods. The generality of the proposed method, however, suggests that it could be applied in other contexts as well.

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  • Strid, Ingvar, 2010. "Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2814-2835, November.
  • Handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2814-2835
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    Cited by:

    1. Mahani, Alireza S. & Sharabiani, Mansour T.A., 2015. "SIMD parallel MCMC sampling with applications for big-data Bayesian analytics," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 75-99.
    2. Edward Herbst & Frank Schorfheide, 2014. "Sequential Monte Carlo Sampling For Dsge Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1073-1098, November.
    3. White, Gentry & Porter, Michael D., 2014. "GPU accelerated MCMC for modeling terrorist activity," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 643-651.
    4. Guido Ascari & Paolo Bonomolo & Hedibert Lopes, 2018. "Walk on the wild side: Multiplicative sunspots and temporarily unstable paths," DNB Working Papers 597, Netherlands Central Bank, Research Department.
    5. Sergei Seleznev, 2016. "Solving DSGE models with stochastic trends," Bank of Russia Working Paper Series wps15, Bank of Russia.

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