Hybrid Parallel Sequential Monte Carlo Algorithm Combining MCMC and Auxiliary Variable

摘要

Sequential Monte Carlo (SMC) simulations are widely used to solve problems associated with complex probability distribution. Intensive computations are their main drawbacks, which restrict to be applied to real time applications, and thus efficient parallelism under high performance computing environment is crucial to effective implementations, especially for intelligent computer vision systems. The combination of auxiliary variables importance sampling with Markov Chain Monte Carlo (MCMC) resampling for pipelining data are proposed in this paper so as to minimize executive time, whilst improve the estimation accuracy. Experimental resultion a network of workstations composed of simple off-the-shelf hardware components show that the hybrid parallel scheme provides a bottleneck free to reduce executive time with increasing particles, compared to the conventional SMC and MCMC based parallel schemes.