  DeeProb-kit is a unified library written in Python consisting of a collection
of deep probabilistic models (DPMs) that are tractable and exact
representations for the modelled probability distributions. The availability of
a representative selection of DPMs in a single library makes it possible to
combine them in a straightforward manner, a common practice in deep learning
research nowadays. In addition, it includes efficiently implemented learning
techniques, inference routines, statistical algorithms, and provides
high-quality fully-documented APIs. The development of DeeProb-kit will help
the community to accelerate research on DPMs as well as to standardise their
evaluation and better understand how they are related based on their
expressivity.
