Currently to generate predictions one has to refit the model on missing data, which requires having access to the model object.

It would be quite convenient to be able to update the data of a fitted model using update() (à-la-R), which would allow more flexibility (my use case is that I'm running and saving models locally, and then running some predictions in another step, and currently I need to save the model, the fitted version and the posteriors which is a bit cumbersome).

Would that make sense in Turing? Thanks!

Related, from #2309

• Is it possible to extract the model object/method from the fitted object? In other words, as far as I understand, a Turing model is often defined as a function (which is hard to serialize), which gets turned into a dynamicPPL object through the @model macro. Can we recover/reconstruct that object from the fitted version?