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

arXiv: https://arxiv.org/abs/2002.01184 · HTML: https://mlsanity.com/paper/2002.01184/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2020.

## Built on by (most cited first, 10 of 10)

- [What Are Bayesian Neural Network Posteriors Really Like?](https://mlsanity.com/paper/2104.14421/) (2021)
- [Learning Functional Priors and Posteriors from Data and Physics](https://mlsanity.com/paper/2106.05863/) (2021)
- [NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators](https://mlsanity.com/paper/2208.11866/) (2022)
- [Amortized Bayesian Multilevel Models](https://mlsanity.com/paper/2408.13230/) (2024)
- [Entropy-based adaptive Hamiltonian Monte Carlo](https://mlsanity.com/paper/2110.14625/) (2021)
- [Disentangling impact of capacity, objective, batchsize, estimators, and step-size on flow VI](https://mlsanity.com/paper/2412.08824/) (2024)
- [Mesh-free Eulerian Physics-Informed Neural Networks](https://mlsanity.com/paper/2206.01545/) (2022)
- [Learning to Explore for Stochastic Gradient MCMC](https://mlsanity.com/paper/2408.09140/) (2024)
- [Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers](https://mlsanity.com/paper/2504.02627/) (2025)
- [Efficiently Vectorized MCMC on Modern Accelerators](https://mlsanity.com/paper/2503.17405/) (2025)

## 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.