I have been working on ways to do non-parametric Bayesian updating, which is to say using a previous posterior sample as a prior sample when new data arrive. There is a paper in press in a special issue of Intl J Computational Economics and Econometrics, but as it will be embargoed, I made a web page here: Robert Grant - stats There are several R scripts and Stan models that you are welcome to adapt. They are all CC-BY.

In essence, we can use density estimation trees as a scalable approach for high-dimensional samples. But we need smooth estimates for Stan (or MALA or PDMPs etc etc), so we replace the edges (or convolve them, if you like) with an inverse logistic function:

There is a bandwidth tuning parameter to think about and a translation of the midpoint towards the nearest mode, to control variance inflation. There’s plenty more work to be done on this (listed in the webpage), and y’all are welcome to take bits on. I’m a freelancer so I don’t have much time for methodology.

@Funko_Unko this might address your question which arose some way down in this topic: Fitting ODE models: best/efficient practices? - #65 by Funko_Unko

I will add some more content on ensembles of kudzu sometime soon, in January probably. They provide a big improvement.