Gaussian kernel quadrature at scaled Gauss-Hermite nodes
Provides a practical and theoretically grounded method for kernel quadrature in one dimension and tensor product grids, benefiting applications in Bayesian inference and numerical integration.
The paper derives an accurate, explicit, and numerically stable approximation to Gaussian kernel quadrature weights using scaled Gauss-Hermite nodes, achieving positive weights and proving exponential convergence for functions in the associated RKHS.
This article derives an accurate, explicit, and numerically stable approximation to the kernel quadrature weights in one dimension and on tensor product grids when the kernel and integration measure are Gaussian. The approximation is based on use of scaled Gauss-Hermite nodes and truncation of the Mercer eigendecomposition of the Gaussian kernel. Numerical evidence indicates that both the kernel quadrature and the approximate weights at these nodes are positive. An exponential rate of convergence for functions in the reproducing kernel Hilbert space induced by the Gaussian kernel is proved under an assumption on growth of the sum of absolute values of the approximate weights.