PH-STAT
Fuente:
arXiv
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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866913697053540352 |
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| author | Chung, Moo K. |
| author_facet | Chung, Moo K. |
| contents | We introduce PH-STAT, a comprehensive MATLAB toolbox designed for performing a wide range of statistical inferences and machine learning tasks on persistent homology, primarily for network and graph data, with an emphasis on brain network analysis. Persistent homology is a prominent tool in topological data analysis (TDA) that captures the underlying topological features of complex data sets. The toolbox aims to provide users with an accessible and user-friendly interface for analyzing and interpreting topological data. The Matlab package is distributed in https://github.com/laplcebeltrami/PH-STAT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_05912 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | PH-STAT Chung, Moo K. Algebraic Topology We introduce PH-STAT, a comprehensive MATLAB toolbox designed for performing a wide range of statistical inferences and machine learning tasks on persistent homology, primarily for network and graph data, with an emphasis on brain network analysis. Persistent homology is a prominent tool in topological data analysis (TDA) that captures the underlying topological features of complex data sets. The toolbox aims to provide users with an accessible and user-friendly interface for analyzing and interpreting topological data. The Matlab package is distributed in https://github.com/laplcebeltrami/PH-STAT. |
| title | PH-STAT |
| topic | Algebraic Topology |
| url | https://arxiv.org/abs/2304.05912 |