"number of datapoints, not the size of the inputs" For brain imaging, 100k was 'the dimensionality of the input' (but also the number of datapoints). This coul

"number of datapoints, not the size of the inputs" For brain imaging, 100k was 'the dimensionality of the input' (but also the number of datapoints). This could easily be O(10^6) though with higher res imaging. I'd check out, for discussion: https://www.prowler.io/blog/sparse-gps-approximate-the-posterior-not-the-model https://www.prowler.io/blog/sparse-gps-approximate-the-poste... For code: https://github.com/GPflow/GPflow https://github.com/GPflow/GPflow Based on the people involved knowing what they are talking about, rather than on experience using that particular work.