Scaling properties of a random Yule tree embedded in $\mathbb{R}^{\mathfrak{D}}$
Fuente:
arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866910499217604608 |
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| author | Schoug, Lukas |
| author_facet | Schoug, Lukas |
| contents | We study a random tree, which was introduced by Ajazi et al. as part of a model of a neuronal network. Realising a scaling relation for the law of the tree, we can use elementary techniques to derive asymptotic results on the geometry as time goes to infinity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_05182 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Scaling properties of a random Yule tree embedded in $\mathbb{R}^{\mathfrak{D}}$ Schoug, Lukas Probability 60J80 (Primary) 60G50 (Secondary) We study a random tree, which was introduced by Ajazi et al. as part of a model of a neuronal network. Realising a scaling relation for the law of the tree, we can use elementary techniques to derive asymptotic results on the geometry as time goes to infinity. |
| title | Scaling properties of a random Yule tree embedded in $\mathbb{R}^{\mathfrak{D}}$ |
| topic | Probability 60J80 (Primary) 60G50 (Secondary) |
| url | https://arxiv.org/abs/2312.05182 |