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DOI: 10.14569/IJACSA.2015.060311
PDF

Bootstrap Approximation of Gibbs Measure for Finite-Range Potential in Image Analysis

Author 1: Abdeslam EL MOUDDEN

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 6 Issue 3, 2015.

  • Abstract and Keywords
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Abstract: This paper presents a Gibbs measure approximation method through the adjustment of the associated estimated potential. We use the information criterion to prove the accuracy of this approach and the bootstrap computation method to determine the explicit form. The Gibbs sampler is the tool of our simulations while taking advantage of the use of the only one MCMC inside of the multiple necessary MCMC in the classical approximation. We focus on the validity of our approach for the Gibbs measure of a Markov Random Field with an interaction potential function and the associated uniqueness condition. Some theoretical and numerical results are given.

Keywords: bootstrap computation; Gibbs measure; Markov Chain Monte Carlo; image Analysis; parameter estimation. Likelihood inference

Abdeslam EL MOUDDEN, “Bootstrap Approximation of Gibbs Measure for Finite-Range Potential in Image Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 6(3), 2015. http://dx.doi.org/10.14569/IJACSA.2015.060311

@article{MOUDDEN2015,
title = {Bootstrap Approximation of Gibbs Measure for Finite-Range Potential in Image Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2015.060311},
url = {http://dx.doi.org/10.14569/IJACSA.2015.060311},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {3},
author = {Abdeslam EL MOUDDEN}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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