Translator Disclaimer
26 February 2010 Hybrid parallel sequential Monte Carlo algorithm combining MCMC and auxiliary variable
Author Affiliations +
Proceedings Volume 7546, Second International Conference on Digital Image Processing; 754616 (2010) https://doi.org/10.1117/12.855670
Event: Second International Conference on Digital Image Processing, 2010, Singapore, Singapore
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.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Danling Wang, John Morris, Qin Zhang, and Quanfeng Gu "Hybrid parallel sequential Monte Carlo algorithm combining MCMC and auxiliary variable", Proc. SPIE 7546, Second International Conference on Digital Image Processing, 754616 (26 February 2010); https://doi.org/10.1117/12.855670
PROCEEDINGS
6 PAGES


SHARE
Advertisement
Advertisement
Back to Top