“universal approximators:” for a given continuous function, there exists a neural network that
can approximate it arbitrarily well, given enough neurons (and some additional
assumptions). In contrast, a Bayesian network is a model, but each of its queries can be
viewed as computing a function. In this paper, we identify some key distinctions between the
functions computed by neural networks and those by marginal Bayesian network queries …