A simple tensorflow implementation of forward-backward


#1

I’ve written a fairly simple tensorflow implementation of the forward and backward passes for a standard discrete HMM. It’s a small contribution, but hopefully helpful in some way. What improvements or modifications would make this more useful? (Or, in general, what work would be most useful to improve HMM/time-series support?)


#2

I’ve been looking at implementing infinite hidden state HMMs (see here). This would definitely help with parallelization, would love some help with the rest!


#3

I’d definitely be willing to help out where I can, even if it’s not strictly Edward-related. I’m trying to get a feel for probabilistic state space models of all kinds right now as I think they’ll be necessary for some stuff I’m working on.

My absolute dream would be to have a framework general enough to deal with most of the models and algorithms described by Tom Minka here in a unified way, and composable with other non-dynamic models.