GPR

Gaussian Process Regression (GPR) is a non-parametric Bayesian regression method based on Gaussian processes, designed to learn the mapping relationship between inputs and outputs to achieve predictions of continuous variables. GPR possesses flexible probabilistic modeling capabilities and can provide estimates of the uncertainty associated with predictions, making it widely used in machine learning, time series analysis, and optimization, among other fields. In Miscellaneous tasks, GPR serves as a powerful regression tool, demonstrating significant advantages in handling small sample sizes and nonlinear data.