Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments
Garland Durham and
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Garland Durham: Quantos Analytics, LLC
No 9, Working Paper Series from Economics Discipline Group, UTS Business School, University of Technology, Sydney
Massively parallel desktop computing capabilities now well within the reach of individual academics modify the environment for posterior simulation in fundamental and potentially quite advantageous ways. But to fully exploit these benfits algorithms that conform to parallel computing environments are needed. Sequential Monte Carlo comes very close to this ideal whereas other approaches like Markov chain Monte Carlo do not. This paper presents a sequential posterior simulator well suited to this computing environment. The simulator makes fewer analytical and programming demands on investigators, and is faster, more reliable and more complete than conventional posterior simulators. The paper extends existing sequential Monte Carlo methods and theory to provide a thorough and practical foundation for sequential posterior simulation that is well suited to massively parallel computing environments. It provides detailed recommendations on implementation, yielding an algorithm that requires only code for simulation from the prior and evaluation of prior and data densities and works well in a variety of applications representative of serious empirical work in economics and finance. The algorithm is robust to pathological posterior distributions, generates accurate marginal likelihood approximations, and provides estimates of numerical standard error and relative numerical efficiency intrinsically. The paper concludes with an application that illustrates the potential of these simulators for applied Bayesian inference.
Keywords: Graphics processing unit; particle filter; posterior simulation; sequential Monte Carlo; single instruction multiple data (search for similar items in EconPapers)
JEL-codes: C11 C63 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ore
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