  Mixture of experts (MoE) models are a class of artificial neural networks
that can be used for functional approximation and probabilistic modeling. An
important class of MoE models is the class of mixture of linear experts (MoLE)
models, where the expert functions map to real topological output spaces. There
are a number of powerful approximation results regarding MoLE models, when the
output space is univariate. These results guarantee the ability of MoLE mean
functions to approximate arbitrary continuous functions, and MoLE models
themselves to approximate arbitrary conditional probability density functions.
We utilize and extend upon the univariate approximation results in order to
prove a pair of useful results for situations where the output spaces are
multivariate.
