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Kernel-approximation methods in tandem with efficient online classifiers have proven to be a highly effective solution to the occupancy map problem. In this paper we seek to expand upon the work done in this area. Our contributions are twofold. We demonstrate that a Bayesian logistic regression classifier fails to perform better than the more spartan point-estimator/subgradient descent method. On the other hand, Contrastingly,  we show that Bayesian optimisation over the hyperparameters of the model is an incredibly powerful and useful tool for this application.