mlsanityData to 7 Oct 2026

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

arXiv 2002.01184 ↗First posted 4 Feb 2020stat.CO cs.PL stat.MLcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Feb 2020.

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Citing papers per year: 2021: 3; 2022: 2; 2023: 0; 2024: 3; 2025: 2.

10 papers.

  1. 2021What Are Bayesian Neural Network Posteriors Really Like?cited by 307
  2. 2021Learning Functional Priors and Posteriors from Data and Physicscited by 37
  3. 2022NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operatorscited by 33
  4. 2024Amortized Bayesian Multilevel Modelscited by 12
  5. 2021Entropy-based adaptive Hamiltonian Monte Carlocited by 5
  6. 2024Disentangling impact of capacity, objective, batchsize, estimators, and step-size on flow VIcited by 5
  7. 2022Mesh-free Eulerian Physics-Informed Neural Networkscited by 4
  8. 2024Learning to Explore for Stochastic Gradient MCMCcited by 4
  9. 2025Incorporating the ChEES Criterion into Sequential Monte Carlo Samplerscited by 3
  10. 2025Efficiently Vectorized MCMC on Modern Acceleratorscited by 0