Title of article :
A Note on the Identifiability of General Bayesian Gaussian Models
Author/Authors :
Ghatari, Amir Hossein Amirkabir University of Technology , Shabbak, Ashkan Statistical Research and Training Center , Tabrizi, Elham Kharazmi University
Abstract :
The main aim of this paper is to investigate the identifiability of
Bayesian Gaussian regression model. The model is extensively implemented
in the various Bayesian modeling concepts such as model fitting and model
selection approaches. In accordance with the outcomes, the Bayesian Gaussian
model is identifiable when the model's design matrix is full rank.
Keywords :
Posterior distribution , identifiability , Gaussian model , design matrix , Bayesian statistics
Journal title :
Journal of Theoretical and Applied Physics
