<ul><li>Certifying safety in dynamical systems is crucial, but barrier certificates typically require explicit system models.</li><li>A novel approach is proposed for synthesizing barrier certificates for unknown systems with latent states and polynomial dynamics.</li><li>A Bayesian framework is employed, updating a prior in state-space representation using input-output data via a targeted marginal Metropolis-Hastings sampler.</li><li>The resulting samples are used to construct a candidate barrier certificate through a sum-of-squares program, providing probabilistic guarantees for the unknown system.</li></ul>

Barrier Certificates for Unknown Systems with Latent States and Polynomial Dynamics using Bayesian Inference

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