\(N(m, V)\) on \(\mathbb{R}^d\). As a mixing measure over
the mean of a gaussian_family() with covariance \(\Sigma\), it
induces the mixture \(N(m, \Sigma + V)\) in closed form.
print() shows the mean and covariance, summarising the
covariance by its size above eight dimensions.
Examples
fam <- gaussian_family(d = 2L)
prior <- gaussian_dist(mean = c(0, 0), cov = diag(2))
log_density(fam(prior), c(0.5, 0.5))
#> [1] -2.656024