A Stable Manifold MCMC Method for High Dimensions
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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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Mar 2014.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1.
4 papers.
- 2021Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networkscited by 10
- 2022Bayesian Learning via Q-Exponential Processcited by 3
- 2023Spatiotemporal Besov Priors for Bayesian Inverse Problemscited by 3
- 2024Comparison of parallel SMC and MCMC for Bayesian deep learningcited by 1
- 2024Comparison of parallel SMC and MCMC for Bayesian deep learning9 Feb 2024
- 2023Spatiotemporal Besov Priors for Bayesian Inverse Problems28 Jun 2023
- 2022Bayesian Learning via Q-Exponential Process14 Oct 2022
- 2021Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks11 Jan 2021