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GSoC 2024 projects
New contributors should first read the contributing guide and read through some of the examples in Bambi's documentation.
To be considered as a GSoC student, you should make a PR to Bambi. It can be something small, like a doc fix or a simple bug fix.
If you are a student interested in participating, please contact us GitHub discussions
Below is a list of possible topics for your GSoC project, we are also open to other topics, contact us on GitHub discussions
When writing your proposal, choose some specific tasks and make sure your proposal is adequate for the GSoC time commitment. We expect all projects to be 350h projects, if you'd like to be considered for a 175h project contact us. We will not accept 175h applications from people with whom we haven't discussed their time commitments before applying.
Students who work on Bambi can expect their skills to grow in
- Bayesian Inference libraries such as PyMC
- Bayesian modeling
- InfereneData/Xarray usage (depending on the project)
- PyData stack (NumPy, SciPy, Matplotlib, Pandas, etc.)
Projection predictive inference is a method used for variable selection, and it has demonstrated effective performance across various fields. The use provides a reference model, built and fitted with Bambi, with all relevant variables. Then submodels, representing different variable subsets, are automatically created and the reference model's posterior distribution is projected onto these submodels. The smallest submodel with predictions close to the reference model is then selected, providing a balance between simplicity and accuracy in variable selection. Currently, Bambi only supports Projection predictive inference for a few models, for instance, not all families are supported, also hierarchies are not supported. This project aims to expand the width of models that can be used to perform projection predictive inference.
- Osvaldo Martin
- Tomás Capretto
- Hours: 350
- Expected outcome: An expansion of the scope of models suitable for projection predictive inference, not necessarily feature-parity with Bambi's models. One or more notebook examples can be added to Bambi's docs demonstrating the new features and providing useful advice for practitioners.
- Skills required: Python, statistics
- Difficulty: Medium