Dual NUP Representations and Min-Maximization in Factor Graphs
This work provides an incremental improvement for researchers in estimation theory and factor graph modeling.
The paper tackles the problem of model-based estimation by extending normals with unknown parameters (NUP) to factor graphs with convex-dual variables, resulting in a new iterative forward-backward algorithm that is dual to an existing backward-forward method.
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.