kl_mvn function

Kullback-Leibler Divergence

Compute KL divergence for a multivariate normal distribution.

kl_mvn(true, estimate, stein = FALSE)

Arguments

• true: Matrix. The true precision matrix (inverse of the covariance matrix)
• estimate: Matrix. The estimated precision matrix (inverse of the covariance matrix)
• stein: Logical. Should Stein's loss be computed (defaults to TRUE)? Note KL divergence is half of Stein's loss.

Returns

Numeric corresponding to KL divergence.

Note

A lower value is better, with a score of zero indicating that the estimated precision matrix is identical to the true precision matrix.

Examples

# nodes
p <- 20

main <- gen_net(p = p, edge_prob = 0.15)

y <- MASS::mvrnorm(250, rep(0, p), main$cors)

fit_l1 <- ggmncv(R = cor(y),
              n = nrow(y),
              penalty = "lasso",
              progress = FALSE)

# lasso
kl_mvn(fit_l1$Theta, solve(main$cors))

fit_atan <- ggmncv(R = cor(y),
              n = nrow(y),
              penalty = "atan",
              progress = FALSE)

kl_mvn(fit_atan$Theta, solve(main$cors))