MCMC for multi-modal distributions
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912182858416128 |
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| author | Łatuszyński, Krzysztof Moores, Matthew T. Stumpf-Fétizon, Timothée |
| author_facet | Łatuszyński, Krzysztof Moores, Matthew T. Stumpf-Fétizon, Timothée |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05908 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MCMC for multi-modal distributions Łatuszyński, Krzysztof Moores, Matthew T. Stumpf-Fétizon, Timothée Computation 62-08 G.3 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. |
| title | MCMC for multi-modal distributions |
| topic | Computation 62-08 G.3 |
| url | https://arxiv.org/abs/2501.05908 |