Feature request: per-timestep observation uncertainty for Gaussian HMM / ARHMM (heteroscedastic emissions)
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Description
Hi there,
I’m fitting HMM/ARHMM models to latent trajectories from an upstream encoder that outputs:
- mean vector per timestep
- uncertainty (covariance/variance) per timestep
I’m looking for support for known, time-varying observation uncertainty in emissions, e.g.:
y_t given z_t = k is Normal(mean_{t,k}, Sigma_k + R_t)
where:
- Sigma_k is state-specific emission covariance
- R_t is known per-timestep uncertainty from the encoder
Questions:
- Does this already exist in Dynamax?
- If not, is there a recommended extension point (custom emissions / inputs)?
- Is this feature on the roadmap?
If helpful, I’m happy to prototype a PR with guidance on preferred API shape.
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Research direction
No files or tests are named. Start by locating the Gaussian HMM and ARHMM emission entry points and any custom-emission or input extension points, then assess how known per-timestep uncertainty could support the stated Normal(mean, Sigma_k + R_t) behavior. Done would be a reviewed API and implementation supporting time-varying observation uncertainty.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100