pymc-devs / pymc-devs/pymc-examples
Bayesian Decision Analysis (Posterior Predictive + Utility Function)
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- Dominant language
- Python
- Stars
- 398
- Forks
- 325
- Avg merge
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- Merged PRs (30d)
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Description
Notebook proposal
Title: Bayesian Decision Analysis
Why should this notebook be added to pymc-examples?
PyMC lacking an example of a Bayesian Decision Analysis, much in the style of an example in Stan's Users Guide (see reference). I could try to create a Notebook, if that's of interest.
References
https://mc-stan.org/docs/stan-users-guide/example-decision-analysis.html
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 reviewing the pymc-examples notebook collection and the linked Stan Users Guide decision-analysis example. Determine the intended Bayesian decision-analysis scope, including posterior predictive quantities and a utility function, then create a corresponding Python Jupyter notebook. Done means the example is complete, understandable, and consistent with the repository's existing notebooks.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, python
- Domain
- documentation, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100