tensorflow / tensorflow/probability
CholeskyWishart distribution
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Description
These names are subject to change, but basically mimicking what PyMC3:
https://docs.pymc.io/api/distributions/multivariate.html#pymc3.distributions.multivariate.LKJCholeskyCov
and Stan:
https://mc-stan.org/docs/2_19/functions-reference/cholesky-lkj-correlation-distribution.html
In practice you would want samples from a CholeskyLKJ because the computations are more numerically stable (as well as log_prob and other calculations). We have LKJ(input_output_cholesky=True) which takes the cholesky of all outputs, and assumes the inputs are the cholesky of something. This works great for sampling, but the log_prob does not contain a Jacobian correction term (which can lead to different / potentially unwanted behavior), as it simply takes LL^T of the input.
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 the existing LKJ(input_output_cholesky=True) behavior and compare it with the PyMC3 LKJCholeskyCov and Stan Cholesky LKJ references linked in the issue. Done means introducing the proposed CholeskyWishart or CholeskyLKJ behavior with the appropriate Jacobian correction in log_prob, while accounting for the names being subject to change.
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
- 35/100