  Markov chain Monte Carlo (MCMC) is widely regarded as one of the most
important algorithms of the 20th century. Its guarantees of asymptotic
convergence, stability, and estimator-variance bounds using only unnormalized
probability functions make it indispensable to probabilistic programming. In
this paper, we introduce the TensorFlow Probability MCMC toolkit, and discuss
some of the considerations that motivated its design.
