Bayesian Subset Selection for Simplex Distributi on Linear Models
Zhao Yuan-yin · Neimenggu Shi-da xuebao. Zhexue shehui kexue hanwen ban · 2015
Bayesian subset selection for simplex distribution linear models is studied by Gibbs sampler and Metropolis-Hastings algorithm in this paper.Firstly,we define simplex distribution linear models which include model uncertainty;And then we explicitly describe a MCMC procedure that can identify the promising subsets of predictors under the simplex distribution linear models.Numerical examples are used to illustrate the proposed methodology.