I am working on a GARCH estimation with a slight twist. For that I need to use a modified posterior distribution as prior for something else. The posterior distribution from Stan is a sample of vectors. I was wondering if there is some way to take a KDE of these samples and feed it to a Stan model to use as posteriors?
I think using a multivariate normal, multivariate student t, etc. would be a better choice for a prior distribution that approximates a previous posterior distribution, although you may be to obtain the parameters in the unconstrained space. These multivariate distributions are implemented in Stan, whereas kernel density estimators are not. Also, getting reasonable kernel density estimates in a high dimensional parameter space is difficult.