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Title & Speakers	Event
Event [Online] A Tutorial for Getting Started with PyMC 2025-08-12
The PyMC Ecosystem and Resources 2025-08-12 · 16:00 Christopher Fonnesbeck – Principal Quantitative Analyst @ PyMC Labs; Adjunct Associate Professor, Vanderbilt University Medical Center ArviZ for visualization and diagnostics; related packages (Bambi, PyMC-experimental); finding and using PyMC example notebooks; community resources and support channels. pymc arviz bambi pymc-experimental
Future Directions 2025-08-12 · 16:00 Christopher Fonnesbeck – Principal Quantitative Analyst @ PyMC Labs; Adjunct Associate Professor, Vanderbilt University Medical Center How AI/LLMs are changing PyMC workflows; PyMC's development roadmap; opportunities for contribution. pymc ai/llms open-source development
Introduction to PyMC and Probabilistic Programming 2025-08-12 · 16:00 Christopher Fonnesbeck – Principal Quantitative Analyst @ PyMC Labs; Adjunct Associate Professor, Vanderbilt University Medical Center Overview of PyMC and its role in the Python data science ecosystem; understanding probabilistic vs Bayesian approaches; a survey of the probabilistic programming landscape; real-world applications and case studies. pymc arviz Python bayesian statistics
Common Pitfalls and Solutions 2025-08-12 · 16:00 Christopher Fonnesbeck – Principal Quantitative Analyst @ PyMC Labs; Adjunct Associate Professor, Vanderbilt University Medical Center Addressing frequently asked questions; debugging convergence issues; understanding and fixing divergences; performance optimization tips. pymc arviz bayesian statistics performance optimization
PyMC Fundamentals 2025-08-12 · 16:00 Christopher Fonnesbeck – Principal Quantitative Analyst @ PyMC Labs; Adjunct Associate Professor, Vanderbilt University Medical Center Model contexts and random variables; prior and likelihood specification; working with observed data; PyMC's relationship with ArviZ. pymc arviz Python bayesian statistics

Introduction to PyMC and Probabilistic Programming 2025-08-12 · 16:00 Chris Fonnesbeck – Principal Quantitative Analyst @ PyMC Labs This one-hour tutorial introduces new users to version 5 of PyMC, a powerful Python, open source library for probabilistic programming and Bayesian statistical modeling. Participants will learn the fundamentals of PyMC, best practices for installation and setup, and gain hands-on experience building their first Bayesian model. pymc arviz bambi pymc-experimental	[Online] A Tutorial for Getting Started with PyMC
Event [Online] A Tutorial for Getting Started with PyMC 2025-08-12
Building Your First Model 2025-08-12 · 16:00 Christopher Fonnesbeck – Principal Quantitative Analyst @ PyMC Labs; Adjunct Associate Professor, Vanderbilt University Medical Center Hands-on example: Bayesian linear regression; prior predictive checks; posterior sampling with NUTS; basic model diagnostics; posterior predictive checks. pymc nuts arviz Python
Installation and Environment Setup 2025-08-12 · 16:00 Christopher Fonnesbeck – Principal Quantitative Analyst @ PyMC Labs; Adjunct Associate Professor, Vanderbilt University Medical Center Recommended installation procedure; PyMC's computational backends; troubleshooting common installation issues; setting up development environments. pymc Python software installation

Developing Hierarchical Models for Sports Analytics 2023-09-14 · 16:00 🎙️ Speaker: Chris Fonnesbeck\, Thomas Wiecki \\| ⏰ Time: 16:00 UTC / 9am PT / 12pm ET / 6pm Berlin Decision-making in sports has become increasingly data-driven with GPS, cameras, and other sensors providing streams of information at high spatial and temporal resolution. While machine learning is a popular approach for turning these data streams into actionable information, Bayesian statistical methods offer a robust alternative. They allow for the combining of multiple data sources, a natural means for imputing missing data, as well as full accounting for various system uncertainties. In particular, hierarchical models provide a means for integrating information at multiple scales and adjusting for biases associated with small sample sizes. I will demonstrate a Bayesian workflow for model development using PyMC version 5, from data preparation through to the summarization of estimates and predictions, using baseball data. 📜 Outline of Talk / Agenda: 5 min: Intro to PyMC Labs and speakers 45 min: Presentation, panel discussion 10 min: Q&A 💼 About the speaker: Chris Fonnesbeck Chris is the Principal Quantitative Analyst in Baseball Research & Development for the Philadelphia Phillies. He is interested in computational statistics, machine learning, Bayesian methods, and applied decision analysis. He hails from Vancouver, Canada and received his Ph.D. from the University of Georgia. 🔗 Connect with Chris: 👉 LinkedIn: https://www.linkedin.com/in/christopher-fonnesbeck-374a492a/ 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/	Developing Hierarchical Models for Sports Analytics

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