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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2408.15624 |
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| _version_ | 1866916888753209344 |
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| author | Devroye, Luc Lugosi, Gabor Zwiernik, Piotr |
| author_facet | Devroye, Luc Lugosi, Gabor Zwiernik, Piotr |
| contents | We consider the problem of structure recovery in a graphical model of a tree where some variables are latent. Specifically, we focus on the Gaussian case, which can be reformulated as a well-studied problem: recovering a semi-labeled tree from a distance metric. We introduce randomized procedures that achieve query complexity of optimal order. Additionally, we provide statistical analysis for scenarios where the tree distances are noisy. The Gaussian setting can be extended to other situations, including the binary case and non-paranormal distributions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15624 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning latent tree models with small query complexity Devroye, Luc Lugosi, Gabor Zwiernik, Piotr Statistics Theory 62H22 We consider the problem of structure recovery in a graphical model of a tree where some variables are latent. Specifically, we focus on the Gaussian case, which can be reformulated as a well-studied problem: recovering a semi-labeled tree from a distance metric. We introduce randomized procedures that achieve query complexity of optimal order. Additionally, we provide statistical analysis for scenarios where the tree distances are noisy. The Gaussian setting can be extended to other situations, including the binary case and non-paranormal distributions. |
| title | Learning latent tree models with small query complexity |
| topic | Statistics Theory 62H22 |
| url | https://arxiv.org/abs/2408.15624 |