A Bayesian Regression Model and Applications ()
ABSTRACT
A sparse vector regression model is developed. The model is established by employing Bayesian formulation and trained by using a set of data
. The parameters needed to be determined in the algorithm are reduced by a special prior hyperparameter setting, and therefore the algorithm is simpler than similar type of Bayesian vector regression models. The examples of applications to the function approximation and inverse scattering problem are presented.
Share and Cite:
Yu, Y. (2020) A Bayesian Regression Model and Applications.
Journal of Applied Mathematics and Physics,
8, 1877-1887. doi:
10.4236/jamp.2020.89141.
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