Peak Inference for Gaussian Random Fields on a Lattice
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929684472659968 |
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| author | Lin, Tuo Schwartzman, Armin Davenport, Samuel |
| author_facet | Lin, Tuo Schwartzman, Armin Davenport, Samuel |
| contents | In this work we develop a Monte Carlo method to compute the height distribution of local maxima of a stationary Gaussian or Gaussian-related random field that is observed on a regular lattice. We show that our method can be used to provide valid peak based inference in datasets with low levels of smoothness, where existing formulae derived for continuous domains are not accurate. We also extend the methods in Worsley (2005) and Taylor et al. (2007) to compute the peak height distribution and compare them with our approach. Lastly, we apply our method to a task fMRI dataset to show how it can be used in practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13239 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Peak Inference for Gaussian Random Fields on a Lattice Lin, Tuo Schwartzman, Armin Davenport, Samuel Methodology In this work we develop a Monte Carlo method to compute the height distribution of local maxima of a stationary Gaussian or Gaussian-related random field that is observed on a regular lattice. We show that our method can be used to provide valid peak based inference in datasets with low levels of smoothness, where existing formulae derived for continuous domains are not accurate. We also extend the methods in Worsley (2005) and Taylor et al. (2007) to compute the peak height distribution and compare them with our approach. Lastly, we apply our method to a task fMRI dataset to show how it can be used in practice. |
| title | Peak Inference for Gaussian Random Fields on a Lattice |
| topic | Methodology |
| url | https://arxiv.org/abs/2501.13239 |