Gaussian process regression was designed for problems with strictly numeric predictor variables. However, GPR can be used with categorical predictor variables by using one-hot encoding. For example, ...

Understanding Gaussian Process Regression I suspect that one of the reasons why Gaussian process regression is not used as often as other regression techniques is that GPR is very intimidating from a ...

Researchers explored the decision-making process of Gaussian process (GP) models, focusing on loss landscapes and hyperparameter optimization. They emphasized the importance of the Matérn kernel ...

The technique of Gaussian Processes (GP) is widely used to reconstruct cosmological parameters, most notably the expansion rate of the universe, using observational data.

Poisson Process – memoryless properties, alternative definitions, combining and splitting. Finite State Markov chains – first passage time analysis, steady-state analysis Gaussian Processes – jointly ...