dgmrf2: Reparametrised multivariate Gaussian distribution

In LaMa: Fast Numerical Maximum Likelihood Estimation for Latent Markov Models

Reparametrised multivariate Gaussian distribution

Description

Density function of the multivariate Gaussian distribution reparametrised in terms of its precision matrix (inverse variance). This implementation is particularly useful for defining the joint log-likelihood with penalised splines or i.i.d. random effects that have a multivariate Gaussian distribution with fixed precision/ penalty matrix \lambda S. As S is fixed and only scaled by \lambda, it is more efficient to precompute the determinant of S (for the normalisation constant) and only scale the quadratic form by \lambda when multiple spline parameters/ random effects with different \lambda's but the same penalty matrix S are evaluated.

Usage

dgmrf2(x, mu = 0, S, lambda, logdetS = NULL, log = FALSE)

Arguments

x	density evaluation point, either a vector or a matrix
mu	mean parameter. Either scalar or vector
S	unscaled precision matrix
lambda	precision scaling parameter Can be a vector if x is a matrix. Then each row of x is evaluated with the corresponding lambda. This is benefitial from an efficiency perspective because the determinant of S is only computed once.
logdetS	Optional precomputed log determinant of the precision matrix S. If the precision matrix does not depend on parameters, it can be precomputed and passed to the function.
log	logical; if TRUE, densities are returned on the log scale.

Details

This implementation allows for automatic differentiation with RTMB.

Value

vector of density values

Examples

x = matrix(runif(30), nrow = 3)

# iid random effects
S = diag(10)
sigma = c(1, 2, 3) # random effect standard deviations
lambda = 1 / sigma^2
d = dgmrf2(x, 0, S, lambda)

# P-splines
L = diff(diag(10), diff = 2) # second-order difference matrix
S = t(L) %*% L
lambda = c(1,2,3)
d = dgmrf2(x, 0, S, lambda, log = TRUE)

LaMa documentation built on June 8, 2025, 1:53 p.m.