# Unbiased Markov Chain Monte Carlo: what, why, and how

arXiv: https://arxiv.org/abs/2406.06851 · HTML: https://mlsanity.com/paper/2406.06851/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2024.

## Built on by (most cited first, 3 of 3)

- [Linear-cost unbiased posterior estimates for crossed effects and matrix factorization models via couplings](https://mlsanity.com/paper/2410.08939/) (2024)
- [Couple to Control: Joint Initial Noise Design in Diffusion Models](https://mlsanity.com/paper/2605.11311/) (2026)
- [Unbiased Derivative Estimation for Stationary Mean of Parameterized Markov chains](https://mlsanity.com/paper/2606.11487/) (2026)

## Abstract (arXiv)

This document presents methods to remove the initialization or burn-in bias from Markov chain Monte Carlo (MCMC) estimates, with consequences on parallel computing, convergence diagnostics and performance assessment. The document is written as an introduction to these methods for MCMC users. Some theoretical results are mentioned, but the focus is on the methodology.