Flexible Regression Models with Gaussian Process Prior
Anis Fradi, Tien Tam, Tran, Chafik Samir · HAL (Le Centre pour la Communication Scientifique Directe) · 2023
In this paper, we introduce a set of novel data-driven regression models with low complexities. We address the challenges of infeering and learning from a substantial number of observations (N >> 1) with Gaussian process prior. We propose a flexible construction of well adapted covariances originally derived from specific differential operators.