Study on Bayesian Regression Model and Applications

Yu, Yijun (2022) Study on Bayesian Regression Model and Applications. In: Novel Research Aspects in Mathematical and Computer Science Vol. 7. B P International, pp. 123-133. ISBN 978-93-5547-773-6

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A sparse vector regression model is introduced. The algorithm is established by employing Gaussian process and Bayesian formulation. By using a special prior hyperparameter setting in the developing process, the number of parameters in the algorithm is reduced, and generating a relatively simple algorithm compared with similar type of Bayesian vector regression models. The algorithm is done by using computational iterative approach. The examples of applications to the function approximations and the inverse scattering problem are presented.

Item Type: Book Section
Subjects: Eprints STM archive > Computer Science
Depositing User: Unnamed user with email admin@eprints.stmarchive
Date Deposited: 06 Oct 2023 12:45
Last Modified: 06 Oct 2023 12:45

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