  The TensorFlow Distributions library implements a vision of probability
theory adapted to the modern deep-learning paradigm of end-to-end
differentiable computation. Building on two basic abstractions, it offers
flexible building blocks for probabilistic computation. Distributions provide
fast, numerically stable methods for generating samples and computing
statistics, e.g., log density. Bijectors provide composable volume-tracking
transformations with automatic caching. Together these enable modular
construction of high dimensional distributions and transformations not possible
with previous libraries (e.g., pixelCNNs, autoregressive flows, and reversible
residual networks). They are the workhorse behind deep probabilistic
programming systems like Edward and empower fast black-box inference in
probabilistic models built on deep-network components. TensorFlow Distributions
has proven an important part of the TensorFlow toolkit within Google and in the
broader deep learning community.
