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Gaussian relative entropy

Source: R/aci-core.R
aci_metric_pair.Rd

Relative entropy of one multivariate Gaussian from another, optionally split into its signal and dispersion parts.

Usage

aci_metric_pair(mu_p, R_p, mu_q, R_q, decompose = TRUE)

Arguments

mu_p

Numeric vector, mean of the first distribution.

R_p

Covariance matrix of the first distribution.

mu_q

Numeric vector, mean of the second distribution.

R_q

Covariance matrix of the second distribution.

decompose

TRUE to return the signal and dispersion parts alongside the total.

Value

A named numeric vector with the total and, when decompose is TRUE, the signal and dispersion parts.

Details

Public KL values never apply a covariance ridge; callers wanting regularisation opt in explicitly with spd_floor(). andreou2026cir (Section 2.2, closing paragraph) states that regularization is expected only in the degenerate limit, which is the published basis for this function's strictness. The state recursions are strict by default too, and regularise only when a call asks for it with regularize = "floor"; see aci_filter().

See also

aci_metric(), aci()

Examples

aci_metric_pair(mu_p = 0, R_p = matrix(1), mu_q = 1, R_q = matrix(2))
#>      total     signal dispersion 
#> 0.34657359 0.25000000 0.09657359