IEEE

Nonlinear Gaussian filtering via radial basis function approximation

Abstract: This paper presents a novel type of Gaussian filter - the radial basis Gaussian filter (RB-GF) - for nonlinear state estimation. In the RB-GF, we propose to use radial basis functions (RBFs) ...
IEEE

Gaussian radial basis functions and indexed families of functionals

Abstract: In this paper, we report on results concerning the capabilities of Gaussian radial basis function networks in the setting of inner product spaces that need not be finite dimensional.
GitHub

Plots of GRBF and GGRBF Kernels.m

title('Generalized Gaussian Radial Basis Function Kernel; $\sigma=\frac{1}{2}$, $\sigma_0=\frac{1}{0.98}$','Interpreter','latex') contourf(X,Y,GG,50),colorbar ...
JSTOR Daily

Efficient Bayesian Hierarchical Functional Data Analysis with Basis Function Approximations Using Gaussian-Wishart Processes

Functional data are defined as realizations of random functions (mostly smooth functions) varying over a continuum, which are usually collected on discretized grids with measurement errors. In order ...
JSTOR Daily

Linear mixed model for heritability estimation that explicitly addresses environmental variation

The linear mixed model (LMM) is now routinely used to estimate heritability. Unfortunately, as we demonstrate, LMM estimates of heritability can be inflated when using a standard model. To help reduce ...
GitHub

2503.06762.md

Neural fields such as DeepSDF and Neural Radiance Fields have recently revolutionized novel-view synthesis and 3D reconstruction from RGB images and videos. However, achieving high-quality ...