PyMCon Events

PyMC has now multiple options to boost its performance (JAX support, training on GPUs, etc). The library is widely known for being easy to learn and for its great documentation, but it’s not always seen as a performant tool. The goal of the blog post is to present a benchmark where we can show that PyMC can work with large datasets and different approaches to do so. The blog post will be accompanied with reproducible code so that we can add/update metrics when there are substantial changes in PyMC or other libraries. Users will be able to compare their own models using the code provided in the blog repository.

In this talk, Bill will introduce a PyMC Hilbert Space Gaussian Process (HSGP) implementation and show via case studies how it fills a few key gaps in the PyMC GP library: fast GPs as model subcomponents, and fast GPs with non-Gaussian likelihoods. I’ll also cover tips and tricks for applying HSGPs effectively in practice.

The talk aims to get users quickly up and performing GPs, especially multi-output GPs using PyMC. Several examples with time-series datasets are used to illustrate different GPs features. This presentation will allow users to leverage GPs to analyze their data effectively.

This talk will attempt to answer the question what is a Data Generating Process and why does it matter? While we will begin our discussion with a bit of theory, don’t worry about this being too technical or inaccessible if you’re new to Bayesian Statistics. Our primary goal is to focus on the second half of the question and give you tools to use for real-world applications.