Metropolis-Hastings prefetching algorithms

Ingvar Strid ()
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Ingvar Strid: Dept. of Economic Statistics, Stockholm School of Economics, Postal: Stockholm School of Economics, P.O. Box 6501, SE-113 83 Stockholm, Sweden

No 706, SSE/EFI Working Paper Series in Economics and Finance from Stockholm School of Economics

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. In this paper 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 many other contexts as well.

Keywords: Prefetching; Metropolis-Hastings; Parallel Computing; DSGE models; Optimal acceptance rate (search for similar items in EconPapers)
JEL-codes: C11 C13 C63 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2008-12-02, Revised 2009-12-02
New Economics Papers: this item is included in nep-cba, nep-cmp and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published as Strid, Ingvar, 'Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach' in Computational Statistics and Data Analysis, 2010, pages 2814-2835.

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