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Proceedings Paper

Hybrid parallel sequential Monte Carlo algorithm combining MCMC and auxiliary variable
Author(s): Danling Wang; John Morris; Qin Zhang; Quanfeng Gu
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Paper Abstract

Sequential Monte Carlo (SMC) simulations are widely used to solve problems associated with complex probability distribution. Intensive computations are their main drawbacks,whic h restrict to be applied to real time applications,a nd thus efficient parallelism under high performance computing environment is crucial to effective implementations,esp ecially 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,co mpared to the conventional SMC and MCMC based parallel schemes.

Paper Details

Date Published: 26 February 2010
PDF: 6 pages
Proc. SPIE 7546, Second International Conference on Digital Image Processing, 754616 (26 February 2010); doi: 10.1117/12.855670
Show Author Affiliations
Danling Wang, Communication Univ. of China (China)
John Morris, The Univ. of Auckland (New Zealand)
Qin Zhang, Communication Univ. of China (China)
Quanfeng Gu, Institute of Computing Technology (China)


Published in SPIE Proceedings Vol. 7546:
Second International Conference on Digital Image Processing
Kamaruzaman Jusoff; Yi Xie, Editor(s)

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