Join us to explore the cutting-edge development of PyMC, with talks from industry leaders and ample opportunity to connect with like-minded individuals. Don't miss out on this exciting event!
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.
Let’s dig deep into the new options that PyMC has to boost its performance comparing it with other Probabilistic programming frameworks. You will see how to get adequate performance for use cases with large datasets in production.
Are you seriously interested in Bayesian statistics but find yourself relying on familiar frequentist tools when it comes time to present your data at a conference or in a manuscript? If this statement rings true, you are not alone, and this talk is for you! In this presentation, I will go over some new tools we’ve developed to help make running Bayesian versions of some of the most common statistical tests used in the behavioral and neural sciences intuitive and transparent.
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.
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.
Bill Engels
Sandra Yojana Meneses
Hyosub Kim
Dante Gates
Danh Phan