tensorflow / tensorflow/probability
synthetic datapoints sampling from given likelihood function and parameter samples
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Description
Is there an efficient way to sample an artificial datapoint from a given likelihood function and given samples for model parameters? E.g. say that I have a logistic regression model: how can I sample synthetic data given samples from model parameters?
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
No source files, tests, or entry points are identified in the issue. Start by clarifying whether this is a request for an API, documentation, or an example, then identify the existing likelihood and parameter-sampling interfaces involved. Done should include an agreed scope and a reproducible criterion for sampling synthetic datapoints.
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Assessment
- Tech stack
- tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100