A'yunin Sofro
A Gaussian process as prior in regression analysis was developing last decade. The performance of the prediction has shown promising. Recently the data have become large and dependent on each other. It also has nonlinear characteristics. This paper will discuss the extension of the Gaussian process using convolution for multivariate nonlinear regression. The simulation shows that the performance of prediction is good compared with the existed approach. © 2022 American Institute of Physics Inc.. All rights reserved.
Mathematics Department, Universitas Negeri Surabaya, C8 Building, Ketintang Street East Java, Surabaya, 6023, Indonesia
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