3 ms·
> You also can't use Stan for online learning. Can’t you loop posteriors as next iterations priors to get a system that learns online?
by harperlee 6y ago
> You also can't use Stan for online learning.
Can’t you loop posteriors as next iterations priors to get a system that learns online?
- ploika 6y agoYou can, but it's slow and computationally intensive - you still need to be fairly sure that the sampler has converged on the true posterior (I might be a bit off with the terminology there but you know what I mean) before that can become your new prior.
- usgroup 6y agoStan will get your from a prior to a posterior distribution that is best supported by your data, but typically the posterior and prior distributions will not be of the same form, so there's no loop back to make. In the case that your model is extremely simple such that your posterior has a "conjugate prior" (i.e. the posterior and prior are the same family of distribution), this sort of loop back is possible. But where this is possible you have no reason at all to use Stan or MCMC since you can just update your posterior directly.
- harperlee 6y ago"Typically" depends on your problem, right? So if I know that I want to feedback predictions into the next iteration, I need to take care to structure a model that enables that, by having posteriors with same shape as priors. But from what I understand it seems a design consideration, not a fundamental limitation.