First post here. I’m interested in building some tools based off bayesian hidden Markov models. Indeed, what I would really like as a project for myself is a Julia equivalent to the PYHSMM library which is a fantastic library but has some issues/bugs and lacks support.
After following the tutorial for estimating an HMM’s parameters using turing, I wanted to work with longer sequences (think 100-1000 observations). For an EM based HMM this is peanuts. However using the fully bayesian approach as in the tutorial yields impossibly long sampling time.
I can only imagine the bottleneck is sampling the state sequence. Are there approximate approaches to doing this for HMMs?