kernels: Multivariate RBF Kernel

In fido: Bayesian Multinomial Logistic Normal Regression

Multivariate RBF Kernel

Description

Designed to be partially specified. (see examples)

Usage

SE(X, sigma = 1, rho = median(as.matrix(dist(t(X)))), jitter = 1e-10)

LINEAR(X, sigma = 1, c = rep(0, nrow(X)))

Arguments

X	covariate (dimension Q x N; i.e., covariates x samples)
sigma	scalar parameter
rho	scalar bandwidth parameter
jitter	small scalar to add to off-diagonal of gram matrix (for numerical underflow issues)
c	vector parameter defining intercept for linear kernel

Details

Gram matrix G is given by

SE (squared exponential):

G = \sigma^2 * exp(-[(X-c)'(X-c)]/(s*\rho^2))

LINEAR:

G = \sigma^2*(X-c)'(X-c)

Value

Gram Matrix (N x N) (e.g., the Kernel evaluated at each pair of points)

fido documentation built on June 22, 2024, 9:36 a.m.