I’ve been looking to expand on what other HMM libraries can do by having probabilistic estimates with PyMC3. I found some working code for this task here, which was quickly adapted to my data.

I have found runtime to be polynomial with respect to sample length:

This runtime is too slow for my project. Is there a way to adapt this code to make **point estimates** for each timestep’s hidden state, but estimate distributions for the initial, transition and emission matrices which parameterize an HMM? Would that make it asymptotically faster?

I appreciate your help - I’m not very experienced with probabilistic models, but I thought I’d try them on this problem to see if the distributions generated add useful information.

Aside #1: I’m not quite sure which part of the algorithm is pushing it past linear - It seems to be the CategoricalGibbsMetropolis step, is there any documentation on its time complexity?

Aside #2: I am a victim of this bug, is there any reliable fix?