Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.21441 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915639270047744 |
|---|---|
| author | Dolmeta, Patric Giordano, Matteo |
| author_facet | Dolmeta, Patric Giordano, Matteo |
| contents | We study nonparametric Bayesian binary classification, in the case where the unknown probability response function is possibly spatially inhomogeneous, for example, being generally flat across the domain but presenting localized sharp variations. We consider a hierarchical procedure based on the popular Besov-Laplace priors from inverse problems and imaging, with a carefully tuned hyper-prior on the regularity parameter. We show that the resulting posterior distribution concentrates towards the ground truth at optimal rate, automatically adapting to the unknown regularity. To implement posterior inference in practice, we devise an efficient Markov chain Monte Carlo (MCMC) algorithm based on recent ad-hoc dimension-robust methods for Besov-Laplace priors. We then test the considered approach in extensive numerical simulations, where we obtain a solid corroboration of the theoretical results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21441 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical Besov-Laplace priors for spatially inhomogeneous binary classification Dolmeta, Patric Giordano, Matteo Statistics Theory We study nonparametric Bayesian binary classification, in the case where the unknown probability response function is possibly spatially inhomogeneous, for example, being generally flat across the domain but presenting localized sharp variations. We consider a hierarchical procedure based on the popular Besov-Laplace priors from inverse problems and imaging, with a carefully tuned hyper-prior on the regularity parameter. We show that the resulting posterior distribution concentrates towards the ground truth at optimal rate, automatically adapting to the unknown regularity. To implement posterior inference in practice, we devise an efficient Markov chain Monte Carlo (MCMC) algorithm based on recent ad-hoc dimension-robust methods for Besov-Laplace priors. We then test the considered approach in extensive numerical simulations, where we obtain a solid corroboration of the theoretical results. |
| title | Hierarchical Besov-Laplace priors for spatially inhomogeneous binary classification |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2511.21441 |