kernels: Multivariate RBF Kernel
In jsilve24/mongrel: Bayesian Multinomial Logistic Normal Regression

Description Usage Arguments Details Value Examples

Designed to be partially specified. (see examples)

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

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

`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

Gram matrix G is given by

SE (squared exponential):

G = σ^2 * exp(-[(X-c)'(X-c)]/(s*ρ^2))

LINEAR:

G = σ^2*(X-c)'(X-c)

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

  # Create Partial for use with basset
  K <- function(X) SE(X, 2, .2)
  
  # Example use
  X <- matrix(rnorm(10), 2, 5)
  G <- K(X)
  G # this is the gram matrix (the kernel evaluated on a finite set of points)