Study on Bayesian Regression Model and Applications


We introduce a sparse vector regression model. Bayesian formulation and the Gaussian process are used to create the method. When compared to similar Bayesian vector regression models, the number of parameters in the method is minimised by applying a particular prior hyperparameter setting during the development process. The algorithm is created using an iterative computational approach. Examples of applications to the inverse scattering issue and function approximations are given.

Author(s) Details:

Yijun Yu,
Department of Mathematics, Tuskegee University, Tuskegee, AL, USA.

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Keywords: Bayesian regression, gaussian process, maximum likelihood, applications

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