Gaussian Process Regression

Gaussian Process Regression (GPR) is a Bayesian non-parametric method used for regression tasks, aiming to predict a continuous output variable based on input data while providing uncertainty estimates. Current research emphasizes improving GPR's scalability and robustness, focusing on techniques like dividing local Gaussian processes for continual learning, tensor network methods for high-dimensional data, and efficient kernel selection and subsampling strategies. These advancements enhance GPR's applicability across diverse fields, including system identification, time series forecasting, safety-critical control systems, and scientific modeling where accurate uncertainty quantification is crucial.