DocumentCode
518744
Title
Notice of Retraction
Bayesian inference of mixture model via Differential Evolution and sampling
Author
Peng Guo ; Naixiang Li
Author_Institution
Sch. of Comput. Sci. & Technol., Tianjin Univ., Tianjin, China
Volume
4
fYear
2010
fDate
27-29 March 2010
Firstpage
504
Lastpage
508
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Mixture model comprises a finite or infinite number of different distributional types of components and offers a much wider range of modeling possibilities than its components. In his paper, we present an approach for Bayesian inference of mixture model with Differential Evolution and Markov chain Monte Carlo(MCMC). Bayesian inference on Gaussian mixture model via Gibbs sampling and optimization with Differential Evolution MCMC are focuses of our work. The inference framework involves calculations of weight, mean and covariance corresponding to each component. Experimental results show novel effect of our method.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Mixture model comprises a finite or infinite number of different distributional types of components and offers a much wider range of modeling possibilities than its components. In his paper, we present an approach for Bayesian inference of mixture model with Differential Evolution and Markov chain Monte Carlo(MCMC). Bayesian inference on Gaussian mixture model via Gibbs sampling and optimization with Differential Evolution MCMC are focuses of our work. The inference framework involves calculations of weight, mean and covariance corresponding to each component. Experimental results show novel effect of our method.
Keywords
Bayes methods; Gaussian processes; Markov processes; Monte Carlo methods; inference mechanisms; optimisation; sampling methods; Bayesian inference; Gaussian mixture model; Gibbs sampling; Markov chain Monte Carlo; differential evolution; optimization; Agricultural engineering; Bayesian methods; Computational complexity; Computational modeling; Computer science; Frequency; Optimization methods; Probability density function; Sampling methods; Search methods; Bayesian Inference; Differential Evolution; Gibbs sampling; MCMC; Mixture Model;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Computer Control (ICACC), 2010 2nd International Conference on
Conference_Location
Shenyang
Print_ISBN
978-1-4244-5845-5
Type
conf
DOI
10.1109/ICACC.2010.5486898
Filename
5486898
Link To Document