As it stands, our current range of filters explicitly build the covariance matrix in the data space. This scales poorly in the data dimension. However one can calculate updates without building the full covariance matrix, working instead with the samples matrix.

This is something that has not been a priority for us as typically it is good practice to dimensionally reduce data before calibrations. Furthermore the forward model is typically far more expensive. However we may wish to utilize data of size O(10^4)+ in future and so this should be extended