Support for Empirical distributions
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- Python
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Description
Hi,
I have started implementing an Empirical distribution by porting the pyro one.
Kind of unsure how to do the log probability, I actually think the implementation in pyro might be a bit problematic for limited memory GPUs as it artificially creates a N * N tensor where N is the number of diracs in the distribution.
Does anyone have a better idea?
Code as it stands for now:
https://colab.research.google.com/drive/171oY6j290HSRI2NR9rDmVDBfJB2UepKG?usp=sharing
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 linked Colab implementation and the Pyro empirical-distribution implementation it ports. Investigate how log probability should be computed without the reported N × N tensor concern, then clarify the intended API and behavior with maintainers. Done means an agreed, working Empirical distribution implementation with its memory constraints addressed.
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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
- 25/100