Dual NUP Representations and Min-Maximization in Factor Graphs
Machine Learning
2025-04-24 v2 Machine Learning
Systems and Control
Signal Processing
Systems and Control
Abstract
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.
Cite
@article{arxiv.2501.12113,
title = {Dual NUP Representations and Min-Maximization in Factor Graphs},
author = {Yun-Peng Li and Hans-Andrea Loeliger},
journal= {arXiv preprint arXiv:2501.12113},
year = {2025}
}