tensorflow / tensorflow/probability
feature request: support for (matrix)-normal-inverse-wishart
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Description
It would be great if you could implement the (M)NIW code we wrote at https://github.com/probml/ssm-jax/blob/main/ssm_jax/distributions.py#L107 as part of core tfd,
so we can avoid issues such as https://github.com/tensorflow/probability/issues/1617 in the future.
Conjugate Bayesian updating of the parameters (as in https://github.com/probml/ssm-jax/blob/main/ssm_jax/distributions.py#L282) would also be nice :)
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 with ssm_jax/distributions.py at the referenced lines for the matrix-normal-inverse-Wishart implementation and its conjugate Bayesian updating. Then inspect the core tfd distribution patterns to determine where both capabilities belong. Done means the (M)NIW distribution and the requested updating support are available in core TensorFlow Probability and match the referenced behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100