# Data-driven and Model-based Verification: a Bayesian Identification Approach

arXiv: https://arxiv.org/abs/1509.03347 · HTML: https://mlsanity.com/paper/1509.03347/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2015.

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

- [Promises of Deep Kernel Learning for Control Synthesis](https://mlsanity.com/paper/2309.06569/) (2023)
- [Data-Driven Safety Verification using Barrier Certificates and Matrix Zonotopes](https://mlsanity.com/paper/2504.01007/) (2025)

## Abstract (arXiv)

This work develops a measurement-driven and model-based formal verification approach, applicable to systems with partly unknown dynamics. We provide a principled method, grounded on reachability analysis and on Bayesian inference, to compute the confidence that a physical system driven by external inputs and accessed under noisy measurements, verifies a temporal logic property. A case study is discussed, where we investigate the bounded- and unbounded-time safety of a partly unknown linear time invariant system.