deletions | additions       diff --git a/section_Introduction_The_occupancy_map__.tex b/section_Introduction_The_occupancy_map__.tex  index 0714d83..cd61051 100644  --- a/section_Introduction_The_occupancy_map__.tex  +++ b/section_Introduction_The_occupancy_map__.tex 
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The occupancy map problem learns a probabilistic model of some space. We map every point within to an estimation of the probability that it is occupied. For a primer on the problem, see \cite{ramoshilbert}.  Recently, a novel approach for solving this problem in an effective and on-line manner was proposed. We map The method, called hilbert-maps, transform  a low-dimensional feature vector to a more dense space, which space. This  grants a simple linear classifier strong expressibility and power. The mapping process is further  optimised through the use of kernel-approximator methods. Such a model has been demonstrated to perform very well in terms of accuracy/time tradeoff \cite{ramoshilbert}. It does however suffer from a dependence on a number of hyperparameters. Solving this problem would allow for better results and a wider application.  To this end, in section 2 \textit{Section 2}  we explore a model-shift from frequentist to Bayesian logistic regression formulation. Our goal is to firstly determine whether we can get more accurate models. Also of consideration is whether the paradigmatic shift in our characterisation of uncertainty results in less hyper-parameter dependency. In section 3 \textit{Section 3}  of this paper, our focus is on Bayesian parameter optimisation. We attempt to tackle the hyper-parameter problem more directly through the use of intelligent automatic selection algorithms.