How suitable is PyMC in its currently available versions for modelling continuous emission HMMs?
I am interested in having a framework where I can easily explore model variations, without having to update E- and M-step, and dynamic programming recursions for every change I make to the model.
More specific questions are:
- When modelling an HMM in PyMC can I answer the 'typical' tasks that one would like to solve -- i.e., besides parameter estimation also infer the most likely sequence (as usually done with the Viterbi algorithm), or solve a smoothing problem?
- As compared to an implementation with Expectation Maximization, I would expect a sampling based approach to be slower. If that gives me more flexibility on the model building side, that is fine. I would imagine using PyMC for prototyping models. I am wondering though, if I can expect PyMC to handle inference for models with > 10k observations to finish in any reasonable amount of time.
- Would you recommend starting out with PyMC2 or PyMC3 for model building. I know that the inference engine changed between the version, so I would especially wonder what type of sampler might be more suited.
If you'ld think PyMC is not a good choice for my use case, that definitely helps as an answer as well.
