Wasserstein bounds for non-linear Gaussian filters
Fuente:
arXiv
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917536932560896 |
|---|---|
| author | Karvonen, Toni Särkkä, Simo |
| author_facet | Karvonen, Toni Särkkä, Simo |
| contents | Most Kalman filters for non-linear systems, such as the unscented Kalman filter, are based on Gaussian approximations. We use Poincaré inequalities to bound the Wasserstein distance between the true joint distribution of the prediction and measurement and its Gaussian approximation. The bounds can be used to assess the performance of non-linear Gaussian filters and determine those filtering approximations that are most likely to induce error. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21643 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Wasserstein bounds for non-linear Gaussian filters Karvonen, Toni Särkkä, Simo Statistics Theory Probability Most Kalman filters for non-linear systems, such as the unscented Kalman filter, are based on Gaussian approximations. We use Poincaré inequalities to bound the Wasserstein distance between the true joint distribution of the prediction and measurement and its Gaussian approximation. The bounds can be used to assess the performance of non-linear Gaussian filters and determine those filtering approximations that are most likely to induce error. |
| title | Wasserstein bounds for non-linear Gaussian filters |
| topic | Statistics Theory Probability |
| url | https://arxiv.org/abs/2503.21643 |