# The Bayesian Separation Principle for Data-driven Control

arXiv: https://arxiv.org/abs/2409.16717 · HTML: https://mlsanity.com/paper/2409.16717/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2024.

## Built on by (most cited first, 2 of 2)

- [SOMBRL: Scalable and Optimistic Model-Based RL](https://mlsanity.com/paper/2511.20066/) (2025)
- [Sample-efficient and Scalable Exploration in Continuous-Time RL](https://mlsanity.com/paper/2510.24482/) (2025)

## Abstract (arXiv)

In this paper we investigate the existence of a separation principle between model identification and control design in the context of model predictive control. First, we clarify that such a separation principle holds asymptotically in the number of data in a Fisherian context, and show that it holds universally, i.e. regardless of the data size, in a Bayesian context. Then, by formulating model predictive control within a Gaussian regression framework, we describe how the Bayesian separation principle can be used to derive computable, uncertainty-aware expressions for the control cost and optimal input sequence, thereby bridging direct and indirect data-driven approaches. Numerical results in both linear and nonlinear scenarios illustrate that the proposed approach outperform nominal methods that neglect uncertainty, highlighting the advantages of incorporating uncertainty in the control design process.