The reviewed record of science sign in
Pith

arxiv: 2002.01184 · v1 · pith:26DDC2OT · submitted 2020-02-04 · stat.CO · cs.PL· stat.ML

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
show 2 more authors
Matthew D. Hoffman Joshua V. Dillon
This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:26DDC2OTrecord.jsonopen to challenge →

classification stat.CO cs.PLstat.ML
keywords mcmccarlochainmarkovmodernmonteprobabilityalgorithms
0
0 comments X
read the original 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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. gemlib.mcmc: composable kernels for Metropolis-within-Gibbs sampling schemes

    stat.CO 2026-05 unverdicted novelty 7.0

    gemlib.mcmc supplies composable kernel abstractions for Metropolis-within-Gibbs sampling via writer monads, allowing concise expression and reuse of complex MCMC algorithms for partially observed epidemic models.