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GSoC 2025 projects
New contributors should first read the contributing guide and learn the basics of PyTensor. Also they should read through some of the examples in the PyMC docs.
To be considered as a GSoC student, you should make a PR to PyMC / PyTensor. It can be something small, like a doc fix or simple bug fix. Some beginner friendly issues can be found here.
If you are a student interested in participating, please contact us via our Discourse site.
Below there is a list of possible topics for your GSoC project, we are also open to other topics, contact us on Discourse. Keep in mind that these are only ideas and that some of them can't be completely solved in a single GSoC project. 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 you must reach out on Discourse. We will not accept 175h applications from people with whom we haven't discussed their time commitments before submitting the application.
This project will build on previous GSoC projects to continue improving PyMCs support for modeling spatial processes. There are many possible algorithms one may choose to work on, such as Gaussian process based methods for point processes like Nearest Neighbor GPs or the Vecchia approximation, and models that are types of Gaussian Markov Random Fields, like CAR, ICAR and BYM models. Implementations of these can be found in the R package CARBayes and INLA.
- Bill Engels
- Chris Fonnesbeck
- Hours: 350
- Expected outcome: An implementation of one or more of the methods listed above, along with one or more notebook examples that can be added to the PyMC docs demonstrating these techniques.
- Skills required: Python, statistics, GPs
- Difficulty: Medium
Linear state space models offer a general framework for implementing a huge number of time series models in PyMC. PyMC-Experimental currently has a statespace module that implements SARIMAX, VARMAX, and structural models. The module helps users with estimation, forecasting, and causal analysis using these models.
Currently the module does not match all statespace models offered in the statsmodels.tsa.statespace module. In particular, dynamic factor models. This project could implement one or both of these models in the existing statespace framework.
In addition, the project would produce an example notebook showing how to do analysis with the new model, similar to the SARIMAX notebook found here.
This project will require interacting with PyTensor, which is the backend used by PyMC. See https://www.pymc.io/projects/docs/en/v5.0.2/learn/core_notebooks/pymc_pytensor.html for more details. An understanding of time series analysis is also helpful, but not a requirement (you can learn as you go).
- Jesse Grabowski
- Hours: 350
- Expected outcome: New statespace model(s) in the
pymc_experimental.statespacemodule - Skills required: Python; time series econometrics
- Difficulty: Medium