| Title: |
Geodesic Slice Sampler for Multimodal Distributions with Strong Curvature |
| Authors: |
Williams, Bernardo, Yu, Hanlin, Luu, Hoang Phuc Hau, Arvanitidis, Georgios, Klami, Arto |
| Publication Year: |
2025 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Machine Learning |
| More Details: |
Traditional Markov Chain Monte Carlo sampling methods often struggle with sharp curvatures, intricate geometries, and multimodal distributions. Slice sampling can resolve local exploration inefficiency issues and Riemannian geometries help with sharp curvatures. Recent extensions enable slice sampling on Riemannian manifolds, but they are restricted to cases where geodesics are available in closed form. We propose a method that generalizes Hit-and-Run slice sampling to more general geometries tailored to the target distribution, by approximating geodesics as solutions to differential equations. Our approach enables exploration of regions with strong curvature and rapid transitions between modes in multimodal distributions. We demonstrate the advantages of the approach over challenging sampling problems. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2502.21190 |
| Accession Number: |
edsarx.2502.21190 |
| Database: |
arXiv |
