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.

Gaussian process regression is a sophisticated technique that uses what is called the kernel trick to deal with complex non-linear data, and L2 regularization to avoid model overfitting where a model ...

We introduce methods for identifying anomalous patterns in non-iid data by combining Gaussian processes with novel log-likelihood ratio statistic and subset scanning techniques. Our approaches are ...

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

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 ...