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:: Volume 6, Issue 2 (3-2013) ::
J. of Stat. Sci. 2013, 6(2): 187-200 Back to browse issues page
A Multivariate Bayesian Model for Gene Networks
Abdollah Safari , Ali Sharifi , Hamid Pezeshk , Peyman Nickchi , Sayed-Amir Marashi , Changiz Eslahchi
Abstract:   (9779 Views)

There are several methods for inference about gene networks, but there are few cases in which the historical information have been considered. In this research we deal with Bayesian inference on gene network. We apply a Bayesian framework to use the available information. Assuming a proper prior distribution and taking the dependency of parameters into account, we seek a model to obtain promising results. We also deal with the hyper parameter estimation. Two methods are considered. The results will be compared by the use of a simulation based on Gibbs sampler. The strengths and weaknesses of each method are briefly mentioned.

Keywords: Gene network, Bayesian inference, Covariance matrix, Multivariate models, Gibbs sampler
Full-Text [PDF 857 kb]   (1672 Downloads)    
Type of Study: Applied | Subject: Applied Statistics
Received: 2013/01/2 | Accepted: 2013/11/3 | Published: 2013/11/3
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Safari A, Sharifi A, Pezeshk H, Nickchi P, Marashi S, Eslahchi C. A Multivariate Bayesian Model for Gene Networks. J. of Stat. Sci.. 2013; 6 (2) :187-200
URL: http://jss.irstat.ir/article-1-174-en.html


Volume 6, Issue 2 (3-2013) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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