12-02, 10:00–12:00 (UTC), Workshop/Tutorial I
There is a rich ecosystem of libraries for Bayesian analysis in Python and it is necessary to use multiple libraries at the same time to use a Bayesian workflow, from model creation to presenting results going through sampling and model checking.
This working session aims to bring together practitioners to discuss and address interoperability issues within the ecosystem. Attendees should expect a hands-on get together where they will meet other Bayesian practitioners with whom to discuss the issues faced and contribute to open source libraries with issues, pull requests and discussions.
The audience of this workshop are Bayesian practitioners or people interested in Bayesian analysis who are already comfortable with the core libraries of the scientific python ecosystem.
The workshop will start assessing which libraries are used by the attendees and explaining the support we can provide them for each of the libraries used, i.e. advise on how/where is it best to let maintainers know about interoperability issues, strategies to fix existing issues or support in submitting PRs.
We will then have an unstructured time/brainstorming session for attendees to discuss interoperability issues and choose what to work on. The goal is for attendees to form groups of 2-3 people and work together on 1-2 issues they agree on. We expect these initial activities to take 30-40 minutes, the rest of the time will be dedicated to group working.
We will have a few maintainers from multiple libraries in the ecosystem who will be available to provide support during the rest of the working session. Attendees will be able to discuss design ideas and limitations, get help with the contributing process (which may be a bit different for each library), get reviews on the proof of concept implementations to overcome the interoperability issues (potentially from maintainers of multiple libraries) .
Previous knowledge expected
My name is Oriol Abril Pla. I have a background in engineering physics and astrophysics but I currently work as computational statistician. I am a core contributor and council member of ArviZ and PyMC projects. I have also worked on statistical research while at Helsinki University, especially in the fields of inference diagnostics, prior elicitation and data visualization.
I dedicate a lot of my time to community management and documentation because I believe they are as important as the code. I have helped organized and mentored in multiple Data Umbrella sprints. I have also mentored many new ArviZ and PyMC team members whose backgrounds ranged from computational scientist to technical writer.