In this project, we investigate the application of Gaussian process models for decision-making under uncertainty. We consider the setting where the true parameters of the Gaussian process must be inferred whilst simultaneously identifying the maxima of the Gaussian process through noisy observations. The project intends to extend the algorithms considered in previous projects to the setting where the true parameters lie in a continuous space. Sampling from the continuous posterior is a computationally demanding problem.