  Variational inference uses optimization, rather than integration, to
approximate the marginal likelihood, and thereby the posterior, in a Bayesian
model. Thanks to advances in computational scalability made in the last decade,
variational inference is now the preferred choice for many high-dimensional
models and large datasets. This tutorial introduces variational inference from
the parametric perspective that dominates these recent developments, in
contrast to the mean-field perspective commonly found in other introductory
texts.
