MCMC for multi-modal distributions
Computation
2025-01-13 v1
Abstract
We explain the fundamental challenges of sampling from multimodal distributions, particularly for high-dimensional problems. We present the major types of MCMC algorithms that are designed for this purpose, including parallel tempering, mode jumping and Wang-Landau, as well as several state-of-the-art approaches that have recently been proposed. We demonstrate these methods using both synthetic and real-world examples of multimodal distributions with discrete or continuous state spaces.
Cite
@article{arxiv.2501.05908,
title = {MCMC for multi-modal distributions},
author = {Krzysztof Łatuszyński and Matthew T. Moores and Timothée Stumpf-Fétizon},
journal= {arXiv preprint arXiv:2501.05908},
year = {2025}
}
Comments
Will appear in "Handbook of Markov Chain Monte Carlo", 2nd edition