Stationary Kernel

Stationary kernels are fundamental components of Gaussian processes (GPs), used for modeling functions and time series where statistical properties remain consistent across different locations or time points. Current research focuses on extending the capabilities of stationary kernels by developing non-stationary alternatives, often employing spectral mixture models, deep kernel architectures, or spike-and-slab priors to capture complex, dynamic relationships in data. These advancements improve the flexibility and accuracy of GP models, particularly for applications involving non-stationary data such as spatio-temporal processes and high-dimensional time series, leading to more robust predictions and uncertainty quantification in diverse fields.