Can one diagnose a model sequentially over an dataset per datapoint?

Is there a way to first e.g run a model on an dataset then add a datapoint and train it on this single new datapoint?
Or do you have to handcraft the new priors from the posterior which were previously calculated?

You can take a look at the notebook covering the updating of priors. You should also check out the new histogram_utils() distributions in pymx.