Unless I'm missing something, and I likely am, the linked paper is still based on the the fundamental assumptions behind curve fitting that I listed above. Namely, their optimization algorithms, metrics, and models are still based on Hilbert spaces even though they've added stochastic elements and more sophisticated models.

Interesting abstract. I love Bayesian stats so hopefully this will be a fun commute read. Thanks!