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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.12113 |
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| _version_ | 1866910917125472256 |
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| author | Li, Yun-Peng Loeliger, Hans-Andrea |
| author_facet | Li, Yun-Peng Loeliger, Hans-Andrea |
| contents | Normals with unknown parameters (NUP) can be used to convert nontrivial model-based estimation problems into iterations of linear least-squares or Gaussian estimation problems. In this paper, we extend this approach by augmenting factor graphs with convex-dual variables and pertinent NUP representations. In particular, in a state space setting, we propose a new iterative forward-backward algorithm that is dual to a recently proposed backward-forward algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dual NUP Representations and Min-Maximization in Factor Graphs Li, Yun-Peng Loeliger, Hans-Andrea Machine Learning Systems and Control Signal Processing Normals with unknown parameters (NUP) can be used to convert nontrivial model-based estimation problems into iterations of linear least-squares or Gaussian estimation problems. In this paper, we extend this approach by augmenting factor graphs with convex-dual variables and pertinent NUP representations. In particular, in a state space setting, we propose a new iterative forward-backward algorithm that is dual to a recently proposed backward-forward algorithm. |
| title | Dual NUP Representations and Min-Maximization in Factor Graphs |
| topic | Machine Learning Systems and Control Signal Processing |
| url | https://arxiv.org/abs/2501.12113 |