high performance computing on graphics processing units: hgpu.org

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 benefits 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, filling gaps in the theory to provide a thorough and practical foundation for sequential posterior simulation that is well suited to massively parallel computing environments. It provides a specific 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, and provides accurate marginal likelihood simulation approximations. The paper concludes with an application that illustrates the potential of these simulators for applied Bayesian inference.