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

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...

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Veröffentlicht in:	arXiv.org 2020-02
Hauptverfasser:	Lao, Junpeng, Suter, Christopher, Langmore, Ian, Chimisov, Cyril, Saxena, Ashish, Sountsov, Pavel, Moore, Dave, Saurous, Rif A, Hoffman, Matthew D, Dillon, Joshua V
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Bayesian analysis Computer simulation Markov analysis Markov chains Monte Carlo simulation Statistical analysis
Online-Zugang:	Volltext
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Zusammenfassung:	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.
ISSN:	2331-8422