I don’t have any experience with Hidden Markov Models but I have a problem that I’m trying to solve that I believe fits within the HMM framework. I’ve been reading about HMMs (this resource is particularly nice) and I’ve been playing with some toy examples via HMMBase.jl.
The question I have is, in the real world, how would I know the transition probabilities for a process I’m unable to observe? I can make educated guesses about the observation likelihoods/emission probabilities but in my case, I don’t really have any idea what the transition probabilities would be.
I’ve thought about using a particular data source as a proxy for the hidden process but, if I can do that in a way that’s satisfactory, it seems to me that I would just want to use that data source/model to solve my problem.
Can anyone provide some advice, point me to some resources that can assist, or discuss a similar problem where the transition probabilities had to be approximated somehow?