tensorflow / tensorflow/probability
LinearGaussianStateSpaceModel with multivariate masking
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Description
Is it possible to allow mask (in forward_filter, log_prob, etc) to be multivariate i.e. with dimensions [num_timesteps, observation_size]rather than just[num_timesteps]`? That will allow missing observations within individual variables per time step and will be very helpful. I believe most of the current logic should work with minimal change.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by tracing the LinearGaussianStateSpaceModel entry points named in the issue, especially forward_filter and log_prob, and inspect how the current timestep mask is validated and applied. Compare the existing masking tests, then define coverage for masks shaped [num_timesteps, observation_size] and verify that partially missing observations work across the affected methods.
Written by the indexing model from the issue text.
Assessment
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100