tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware
arXiv:2002.01184
Abstract
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.
Based on extended abstract submitted to PROBPROG 2020