# Arrow of Time in Estimation and Control: Duality Theory Beyond the Linear Gaussian Model

arXiv: https://arxiv.org/abs/2405.07650 · HTML: https://mlsanity.com/paper/2405.07650/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2024.

## Built on by (most cited first, 3 of 3)

- [Dual Filter: A Transformer-like Inference Architecture for Hidden Markov Models](https://mlsanity.com/paper/2505.00818/) (2025)
- [What can we learn from signals and systems in a transformer? Insights for probabilistic modeling and inference architecture](https://mlsanity.com/paper/2508.20211/) (2025)
- [Transformer-like Inference from Optimal Control](https://mlsanity.com/paper/2605.15608/) (2026)

## Abstract (arXiv)

Duality between estimation and control is a foundational concept in Control Theory. Most students learn about the elementary duality -- between observability and controllability -- in their first graduate course in linear systems theory. Therefore, it comes as a surprise that for a more general class of nonlinear stochastic systems (hidden Markov models or HMMs), duality is incomplete. Our objective in writing this article is two-fold: (i) To describe the difficulty in extending duality to HMMs; and (ii) To discuss its recent resolution by the authors. A key message is that the main difficulty in extending duality comes from time reversal in going from estimation to control. The reason for time reversal is explained with the aid of the familiar linear deterministic and linear Gaussian models. The explanation is used to motivate the difference between the linear and the nonlinear models. Once the difference is understood, duality for HMMs is described based on our recent work. The article also includes a comparison and discussion of the different types of duality considered in literature.