# Scalable Monte Carlo for Bayesian Learning

arXiv: https://arxiv.org/abs/2407.12751 · HTML: https://mlsanity.com/paper/2407.12751/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 168 references that are in the map.
- First posted on arXiv 17 Jul 2024.

## Builds on (most cited first, 20 of 20)

- [Stochastic Gradient Hamiltonian Monte Carlo](https://mlsanity.com/paper/1402.4102/) (2014)
- [A Complete Recipe for Stochastic Gradient MCMC](https://mlsanity.com/paper/1506.04696/) (2015)
- [Measuring Sample Quality with Kernels](https://mlsanity.com/paper/1703.01717/) (2017)
- [The promises and pitfalls of Stochastic Gradient Langevin Dynamics](https://mlsanity.com/paper/1811.10072/) (2018)
- [Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo](https://mlsanity.com/paper/1705.08964/) (2017)
- [BlackJAX: Composable Bayesian inference in JAX](https://mlsanity.com/paper/2402.10797/) (2024)
- [Sliced Kernelized Stein Discrepancy](https://mlsanity.com/paper/2006.16531/) (2020)
- [Gradient Estimation with Discrete Stein Operators](https://mlsanity.com/paper/2202.09497/) (2022)
- [On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo](https://mlsanity.com/paper/1802.05431/) (2018)
- [Targeted Separation and Convergence with Kernel Discrepancies](https://mlsanity.com/paper/2209.12835/) (2022)
- [Optimal quantisation of probability measures using maximum mean discrepancy](https://mlsanity.com/paper/2010.07064/) (2020)
- [Controlling Moments with Kernel Stein Discrepancies](https://mlsanity.com/paper/2211.05408/) (2022)
- [The True Cost of Stochastic Gradient Langevin Dynamics](https://mlsanity.com/paper/1706.02692/) (2017)
- [The reproducing Stein kernel approach for post-hoc corrected sampling](https://mlsanity.com/paper/2001.09266/) (2020)
- [Stochastic Gradient MCMC Methods for Hidden Markov Models](https://mlsanity.com/paper/1706.04632/) (2017)
- [Quantifying the accuracy of approximate diffusions and Markov chains](https://mlsanity.com/paper/1605.06420/) (2016)
- [Piecewise-Deterministic Markov Chain Monte Carlo](https://mlsanity.com/paper/1707.05296/) (2017)
- [PDMP Monte Carlo methods for piecewise-smooth densities](https://mlsanity.com/paper/2111.05859/) (2021)
- [The Apogee to Apogee Path Sampler](https://mlsanity.com/paper/2112.08187/) (2021)
- [NuZZ: numerical Zig-Zag sampling for general models](https://mlsanity.com/paper/2003.03636/) (2020)

## Abstract (arXiv)

This book aims to provide a graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC) algorithms, as applied broadly in the Bayesian computational context. Most, if not all of these topics (stochastic gradient MCMC, non-reversible MCMC, continuous time MCMC, and new techniques for convergence assessment) have emerged as recently as the last decade, and have driven substantial recent practical and theoretical advances in the field. A particular focus is on methods that are scalable with respect to either the amount of data, or the data dimension, motivated by the emerging high-priority application areas in machine learning and AI.