tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware
This toolkit provides domain-specific improvements for probabilistic programming practitioners, but it appears incremental as it builds on existing MCMC methods without new algorithmic breakthroughs.
The paper introduces the TensorFlow Probability MCMC toolkit, addressing the need for modern MCMC tools optimized for contemporary hardware, though it does not specify concrete numerical results.
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.