  Dependent nonparametric processes extend distributions over measures, such as
the Dirichlet process and the beta process, to give distributions over
collections of measures, typically indexed by values in some covariate space.
Such models are appropriate priors when exchangeability assumptions do not
hold, and instead we want our model to vary fluidly with some set of
covariates. Since the concept of dependent nonparametric processes was
formalized by MacEachern [1], there have been a number of models proposed and
used in the statistics and machine learning literatures. Many of these models
exhibit underlying similarities, an understanding of which, we hope, will help
in selecting an appropriate prior, developing new models, and leveraging
inference techniques.
