2020

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

Lao, Junpeng, Suter, Christopher, Langmore, Ian et al.

Understand

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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emcee: The mcmc hammer
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Original
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Probabilistic programming in python using pymc3
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Tensorflow distributions
Original
Dillon, J. V., Langmore, I., Tran, D., Brevdo, E., Vasudevan, S., Moore, D., Patton, B., Alemi, A., Hoffman, M., and Saurous, R. A. (2017) · 2017
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Iterative NUTS
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Joint distributions for tensorflow probability
Piponi, D., Moore, D., and Dillon, J. V. (2020) · 2020
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