By Topic

An Improved Markov Chain Monte Carlo Scheme for Parameter Estimation Analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Fang Liu ; Sch. of Comput. Sci. & Technol., Wuhan Univ. of Technol., Wuhan ; Hao Pan ; Desheng Jiang ; Jianzhong Zhou

Aiming at resolving the issue of designing appropriate proposal distribution in Markov Chain Monte Carlo (MCMC) algorithm, an improved MCMC scheme is developed in this paper. The presented scheme employs normal density distribution as proposal distribution to sample in objective function, and together with the historical sampling information, the proposal distribution runs to proper distribution by adaptive self-regulation. The improved scheme is applied to parameter estimation of Pearson-III distribution to figure out the problems of runoff frequency forecast. In the case study of annual runoff frequency calculation of Fengtan reservoir, satisfying results are obtained, and compared with the genetic algorithm and the traditional weight function method, the new scheme can not only provide the proper posterior distribution, but also the related statistical information of parameters, which are useful for parameter estimation of complex modeling and uncertainty analysis.

Published in:

Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on  (Volume:1 )

Date of Conference:

20-22 Dec. 2008