Compressible Spectral Mixture Kernels with Sparse Dependency Structures for Gaussian Processes
Compressible Spectral Mixture Kernels with Sparse Dependency Structures for Gaussian Processes
Spectral mixture kernels comprise a powerful class of optimal kernels for Gaussian processes (GPs) to describe complex patterns. This paper introduces model compression and time- and phase (TP) modulated dependency structures to the original spectral mixture (SM) kernel for improved GP model expressiveness. Specifically, by adopting Bienaym´es identity, we generalize …