  In the realm of supervised learning, Bayesian learning has shown robust
predictive capabilities under input and parameter perturbations. Inspired by
these findings, we demonstrate the robustness properties of Bayesian learning
in the control search task. We seek to find a linear controller that stabilizes
a one-dimensional open-loop unstable stochastic system. We compare two methods
to deduce the controller: the first (deterministic) one assumes perfect
knowledge of system parameter and state, the second takes into account
uncertainties in both and employs Bayesian learning to compute a posterior
distribution for the controller.
