# A Stable Manifold MCMC Method for High Dimensions

arXiv: https://arxiv.org/abs/1403.7711 · HTML: https://mlsanity.com/paper/1403.7711/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2014.

## Built on by (most cited first, 4 of 4)

- [Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks](https://mlsanity.com/paper/2101.03906/) (2021)
- [Bayesian Learning via Q-Exponential Process](https://mlsanity.com/paper/2210.07987/) (2022)
- [Spatiotemporal Besov Priors for Bayesian Inverse Problems](https://mlsanity.com/paper/2306.16378/) (2023)
- [Comparison of parallel SMC and MCMC for Bayesian deep learning](https://mlsanity.com/paper/2402.06173/) (2024)

## Abstract (arXiv)

We combine two important recent advancements of MCMC algorithms: first, methods utilizing the intrinsic manifold structure of the parameter space; then, algorithms effective for targets in infinite-dimensions with the critical property that their mixing time is robust to mesh refinement.