Oriol Abril Pla
Oriol is a computational statistician, working as a maintainer of the ArviZ and PyMC libraries and as Principal Data Scientist with PyMC Labs. He started in academia but after some years but he left after some years in order to be able to work more freely and collaboratively on open source, software and knowledge sharing. His main areas of interest are data visualization, model and inference diagnostics, model comparison, and prior elicitation. Within open source projects, he has also dedicated a large part of his work to documentation, governance and DEI.
Sessions
Posterior predictive checks are a key step within Bayesian modeling workflows where we compare model predictions with the data used to fit the model. By focusing on distributional comparisons instead of point estimates, they offer valuable insights about our models, where they fail and inform model improvements. Knowing a model is not completely right is relatively easy, knowing why that is the case and how to fix it are a whole other question which will be the focus of the tutorial. This tutorial will provide data scientists and researchers with multiple strategies for posterior predictive checks to allow their use in continuous, discrete or categorical data, and for homogeneous or heterogeneous data.
Come build something with the PyMC development team.
Code sprints are collaborative working sessions where contributors of all experience levels tackle meaningful open issues side by side. Whether you want to squash a long-standing bug, sharpen the documentation, build a worked example, or simply understand how a major open-source project operates from the inside — there's a place for you here.
PyMC is the most widely used probabilistic programming library in Python, and the people who build it will be in the room. Bring your laptop; we'll handle the rest.