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Add support for hierarchical hidden Markov models (not just hierarchical priors on the transitions and observations) #49
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Hi Shruthi, Thanks for raising this issue. The "hierarchical" extensions are a bit poorly named, unfortunately. They refer to having a hierarchical prior distribution on the observation and/or transition distributions, and they interact with the That said, it wouldn't be too hard to put together a simple hierarchical HMM with multiple levels of discrete states. That's something I've wanted to do for a long time. The straightforward implementation is to just expand the state space to make it a cross product of the higher and lower levels, but this would yield suboptimal message passing performance. A proper HHMM message passing algorithm would be a very feature to add. I'll mark this as a feature request for now and hope to get to it asap! |
Hi Scott, Thanks for clarifying that! I'm a big fan of your work with the worm data :) Yeah, I think the HHMM feature would be really helpful to model nested processes/actions along multiple timescales. I'll keep an eye out for that feature when you add it! |
Is this implemented by now? :) |
Hello,
Thanks for creating this package! I was wondering if you could help me with an example to create a hierarchical HMM? I'm looking to have two levels of states - the higher governs the structure of the transitions between the lower states with a simple Gaussian observation model - I was wondering how to instantiate such a model.
Thanks again!
Shruthi
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