Implementing GPTs in Probabilistic Programming: Separating Inference from Model
Details
🎙️ Speaker: Daniel Lee, Thomas Wiecki | ⏰ Time: 16:00 UTC / 9am PT / 12pm ET / 6pm Berlin
This will be a high-level talk discussing the separation of statistical models and inference algorithms.
Things we’d like to talk about:
- The general vernacular combines two concepts together: model + inference. But they can be thought of separately.
- Given a statistical model, there are (at least) 3 different types of inference. Optimization, approximate inference, Bayesian inference. We’ll talk about some of the use cases of each. And where stochastic optimization fits in.
- A description of GPTs and how it can be implemented in Stan (and similarly in PyMC or any other PPL).
This talk won’t be overly technical. The goal will be to try to solidify the differences between the different types of inference and when to apply them. There will be plenty of time for Q&A.
📜 Outline of Talk / Agenda:
- 5 min: Intro to PyMC Labs and speakers
- 45 min: Presentation, panel discussion
- 10 min: Q&A
💼 About the speaker:
- Daniel Lee
Daniel Lee is at Zelus Analytics working on player projection models across multiple sports. Daniel is a computational Bayesian statistician who helped create and develop Stan, the open-source statistical modeling language with over 20 years of experience in numeric computation and software; over 10 years of experience creating and working with Stan; and 5 years working on pharma-related models including joint models for estimating oncology treatment efficacy and PK/PD models. Past projects have covered estimating vote share for state and national elections; clinical trials for rare diseases and non-small-cell lung cancer; satellite control software for television and government; retail price sensitivity; data fusion for U.S. Navy applications; sabermetrics for an MLB team; and assessing “clutch” moments in NFL footage. He holds a B.S. in Mathematics with Computer Science from MIT, and a Master of Advanced Studies in Statistics from Cambridge University.
🔗 Connect with Daniel Lee:
👉 LinkedIn: https://www.linkedin.com/in/syclik/
👉 Twitter: https://twitter.com/djsyclik
👉 GitHub: https://github.com/syclik
👉 Website: https://syclik.com/
👉 Blog: https://medium.com/@bayesianops
2. Dr. Thomas Wiecki (PyMC Labs)
Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.
🔗 Connect with Thomas Wiecki:
👉 GitHub: https://github.com/twiecki
👉 Twitter: https://twitter.com/twiecki
👉 Website: https://twiecki.io/
📖 Code of Conduct:
Please note that participants are expected to abide by PyMC's Code of Conduct.
🔗 Connecting with PyMC Labs:
👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/
🐦 Twitter: https://twitter.com/pymc_labs
🎥 YouTube: https://www.youtube.com/c/PyMCLabs
🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/
🔗 Connecting with PyMC Open Source:
💬 Q&A/Discussion: https://discourse.pymc.io
🐙 GitHub: https://github.com/pymc-devs/pymc
💼 LinkedIn: https://www.linkedin.com/company/pymc/mycompany
🐥 Twitter: https://twitter.com/pymc_devs
📺 YouTube: https://www.youtube.com/c/PyMCDevelopers
🎉 Meetup: https://www.meetup.com/pymc-online-meetup/