  Consider $\boldsymbol X \sim \mathcal{N}(\boldsymbol 0, \boldsymbol \Sigma)$
and $\boldsymbol Y = (f_1(X_1), f_2(X_2),\dots, f_d(X_d))$. We call this a
diagonal transformation of a multivariate normal. In this paper we compute
exactly the mean vector and covariance matrix of the random vector $\boldsymbol
Y.$ This is done two different ways: One approach uses a series expansion for
the function $f_i$ and the other a transform method. We compute several
examples, show how the covariance entries can be estimated, and compare the
theoretical results with numerical ones.
