The Power of Bayes in Industry: Your Business Model is Your Data Generating Process
Speaker: Dante Gates
Event type: Live webinar
Date: Feb 9th, 2023 (subscribe here for email updates)
Time: 21:00 UTC (4pm EST)
Register for the event: Meetup event or Zoom
Notebook: On Colab
NOTE: The event will be recorded. Subscribe to the PyMC YouTube for notifications.
Video: Interview with Dante Gates (8 minutes)
Video: The Power of Bayes in Industry
Welcome to the first event of the PyMCon Web Series! As part of this series, most events will have an async component and a live talk.
In this case, Dante, as part of the async component, prepared a Colab notebook for the community to engage in before the talk. Run it and answer the questions Dante left for discussion:
- What is your favorite example of a Data Generating Process (DGP) / first principles model?
- Have you applied the ideas in this post in industry?
- What are some of the benefits we missed?
Abstract of the talk
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.
With the core concepts and background covered, we’ll demonstrate how incorporating this understanding into our modeling decisions allows us to embed elements of a business function directly into our statistical models and how this can provide immense value in industry settings, especially where traditional machine learning techniques fail, such as
The ability to tackle critical problems when data is lacking, like launching a new product
Building powerful, predictive models that are difficult to overfit
Explainability is built in, and it’s already expressed in the terms of your business
Best of all is that the design techniques we propose here are such that when you get one the benefits above, the rest usually come for free.
All of this and more will be illustrated through concrete examples found in both publicly available data as well as proprietary data we use here at Perpay.