Gibbsian polar slice sampling
2023-02-08 | conference paper. A publication with affiliation to the University of Göttingen.
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Details
- Authors
- Schär, Philip; Habeck, Michael ; Rudolf, Daniel
- Abstract
- Polar slice sampling (Roberts & Rosenthal, 2002) is a Markov chain approach for approximate sampling of distributions that is difficult, if not impossible, to implement efficiently, but behaves provably well with respect to the dimension. By updating the directional and radial components of chain iterates separately, we obtain a family of samplers that mimic polar slice sampling, and yet can be implemented efficiently. Numerical experiments for a variety of settings indicate that our proposed algorithm outperforms the two most closely related approaches, elliptical slice sampling (Murray et al., 2010) and hit-and-run uniform slice sampling (MacKay, 2003). We prove the well-definedness and convergence of our methods under suitable assumptions on the target distribution.
- Issue Date
- 8-February-2023
- Publisher
- MLResearchPress
- Journal
- Proceedings of Machine Learning Research
- Project
- SFB 1456: Mathematik des Experiments: Die Herausforderung indirekter Messungen in den Naturwissenschaften
SFB 1456 | Cluster A | A05: Statistical methods for the reconstruction of continuous movements from cryo-EM data
SFB 1456 | Cluster B | B02: Ensemble inference – new sampling algorithms and applications in structural biology - Conference
- 40th International Conference on Machine Learning
- Conference Place
- Honolulu, Hawaii, USA
- Event start
- 2023-07-23
- Event end
- 2023-07-29
- Fulltext
- https://proceedings.mlr.press/v202/schar23a.html