Is there a clear implementation of multivariate data into TFP’s distribution.HiddenMarkovModel? Despite repeated attempts I have yet to find any example in official documentation or publicly available repositories of the hidden Markov model in TensorFlow Probability being used with correlated data (Specifically for my use case, multiple time-series). I know of similar implementations in other libraries (Pyro, pymc3, etc.) however it would be preferable for my situation to stay in the TensorFlow environment.

Furthermore, going through the source code for the HMM, it does seem event_shape for ‘observation_distribution’ is utilized, but more in relation to num_steps than for the purpose of interpreting multivariate data?

It should ‘just work’ to build an HMM using an observation distribution with multivariate (vector, matrix, etc) events. If the observation distribution has event shape [d] at each timestep, then the HMM as a whole will have event shape [num_steps, d].

I threw together a quick example of fitting an HMM with multivariate normal emissions here (code also copied below):