  Goodness-of-fit tests are often used in data analysis to test the agreement
of a distribution to a set of data. These tests can be used to detect an
unknown signal against a known background or to set limits on a proposed signal
distribution in experiments contaminated by poorly understood backgrounds.
Out-of-the-box non-parametric tests that can target any proposed distribution
are only available in the univariate case. In this paper, we discuss how to
build goodness-of-fit tests for arbitrary multivariate distributions or
multivariate data generation models.
