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arXiv · 2002.01184

tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware

Junpeng Lao·Christopher Suter·Ian Langmore·Cyril Chimisov·Ashish Saxena·Pavel Sountsov·Dave Moore·Rif A. Saurous·Matthew D. Hoffman·Joshua V. Dillon
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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.

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Junpeng Lao, Christopher Suter, Ian Langmore, Cyril Chimisov, Ashish Saxena, Pavel Sountsov, Dave Moore, Rif A. Saurous, Matthew D. Hoffman, Joshua V. Dillon. 2020-02-04. tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware. https://arxiv.org/abs/2002.01184

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