In our approach, we utilize the Adaptive Experimentation Platform (Ax) to conduct Bayesian optimization. We selected a fully Bayesian model that enables inference of hyperparameters from their posterior distribution. This differs from more traditional approaches that rely on maximum a posteriori probability (MAP) estimates, where only a single value is determined for each hyperparameter. By sampling from the posterior distribution of the hyperparameters, we account for uncertainty in the hyperparameters, thereby constructing a more robust surrogate model.