Foreman-Mackey and I had a long discussion about how to normalize smoothness priors. That is, if you just "regularize" a fit using differences between bin heights (think: making a smooth histogram), it is hard to compute analytically the resulting implicit prior. In the end we decided to use a proper Gaussian Process prior on our histogram bin heights, because then at least the normalization is a determinant, and we can now compute those super fast. In general: If you can solve a problem with a mature technology or else invent something yourself, you should use the mature technology! In this case, that's Gaussian Processes.