Normalizing flows are powerful non-parametric statistical models that function as a hybrid between density estimators and generative models.Current learning algorithms for normalizing flows assume that data points are sampled independently, an assumption that is frequently violated in practice, which may lead to erroneous density estimation and data generation.We propose a likelihood objective of normalizing flows incorporating dependencies between the data points, for which we derive a flexible…