Figure shows a comparison between the information gained from using covariance for the DFT model (Figure ) and the LSR model (Figure ). There are two clear trends that can be seen. First, between both the DFT and the LSR model, when covariance is considered, the KL divergence statistic decreases. Since we are only using the 1D probability distributions to calculate the KL divergence statistic, this is expected. Including covariance constrains the parameter posteriors. This is illustrated in Figures and. While it is more drastic in the DFT case, it is clear that the inclusion of covariance restricts where the posterior can be located. This reduces the search space, which means that overall there is less information to be gained from the experimental data to inform the posterior.