The class of problems I’m working on (bayesian structural time series), work pretty nicely with ADVI (such as pymc3’s) out of the box, and with the existing inferences in Edward, I’m finding myself tweaking things more often than I’d like. In particular, estimating scale parameters on time series uncertainty, I’m having trouble getting the correct estimates at all. So I’m planning on implementing ADVI in Edward.
Beyond following the Kukucelbir et al (2016) paper and existing implementations in stan and pymc, do existing contributors have any wise words when it comes to subclassing
ed.inferences.VariationalInference? Or perhaps useful supplementary material relating to ADVI specifically?