Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.23505 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912929703526400 |
|---|---|
| author | Brysiewicz, Taylor Kim, Juhee |
| author_facet | Brysiewicz, Taylor Kim, Juhee |
| contents | We consider the problem of recovering a permutation group $G \leq S_n$ from an error-prone sampling process $X$. We model $X$ as an $S_n$-valued random variable, defined as a mixture of the uniform distributions on $G$ and $S_n$ . Our suite of tools recovers properties of $G$ from $X$ and bolsters our main method for recovering $G$ itself. Our algorithms are motivated by the numerical computation of monodromy groups, a setting where such error-prone sampling procedures occur organically. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_23505 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | How to recover a permutation group amidst errors Brysiewicz, Taylor Kim, Juhee Statistics Theory Group Theory 20P05 (Primary) 20-08, 68W20, 20B35 (Secondary) We consider the problem of recovering a permutation group $G \leq S_n$ from an error-prone sampling process $X$. We model $X$ as an $S_n$-valued random variable, defined as a mixture of the uniform distributions on $G$ and $S_n$ . Our suite of tools recovers properties of $G$ from $X$ and bolsters our main method for recovering $G$ itself. Our algorithms are motivated by the numerical computation of monodromy groups, a setting where such error-prone sampling procedures occur organically. |
| title | How to recover a permutation group amidst errors |
| topic | Statistics Theory Group Theory 20P05 (Primary) 20-08, 68W20, 20B35 (Secondary) |
| url | https://arxiv.org/abs/2602.23505 |