PyMC Labs Online Meetup

Debugging Bayesian Inference: An Online and Interactive Approach

Wed, Mar 25 · 11:00 AM EDT

·

Online

Online

🎙️ Speakers: Nathanael Nussbaumer, Markus Böck, Jürgen Cito, Christopher Fonnesbeck, Oriol Abril Pla, Evan Wimpey | ⏰ Time: 15:00 UTC / 8:00 AM PT / 11:00 AM ET / 4:00 PM Berlin

Probabilistic programming enables the formulation of Bayesian models as programs and automates posterior inference. However, identifying and repairing issues with inference is notoriously difficult, requiring deep knowledge and long wait times for MCMC algorithms to finish.

Join PyMC Labs as we host researchers Nathanael Nussbaumer, Markus Böck, and Jürgen Cito to discuss their recent paper, Online and Interactive Bayesian Inference Debugging.

They will introduce INFERLOG HOLMES, an open-source debugger specialized for MCMC workflows. By hooking directly into the inference loop, this tool allows practitioners to analyze the output of a probabilistic program during its execution, rather than waiting for a potentially long inference time to finish.

What you'll learn:

• How to use live debugging views to monitor MCMC sampling in real-time, tracking not just trace plots and marginals, but also continuously updated diagnostics like $\hat{R}$, ESS, and sampler metrics.
• How heuristic rules combine computed MCMC diagnostics and sampler stats (like acceptance rates or divergences) to automatically warn you about inference issues.
• How interactive, online diagnostics can drastically speed up your development cycles by allowing you to cancel bad runs early.

📜 Outline of Talk / Agenda:

• 5 min: Introduction to PyMC Labs and speakers
• 40 min: Panel discussion
• 15 min: Q&A

-------------------------------------------------

💼 About the speakers:

Nathanael Nussbaumer (InferLog Holmes Co-Author)
Nathanael is a master’s student at the Technical University of Vienna. He has been working on probabilistic programming for 4 years with a focus on developer tools for probabilistic programming and Bayesian inference debugging.
🔗 Connect with Nathanael
👉 LinkedIn: https://www.linkedin.com/in/nathanael-nu

Markus Böck (InferLog Holmes Co-Author)
Markus is a PhD student at the Technical University of Vienna, where he sits at the intersection of Computer Science and Mathematics. His research focuses on probabilistic programming, developing sound program analysis methods to enhance debugging and to optimise inference algorithms. Additionally, he explores the use of GPU acceleration to scale the performance of inference algorithms for challenging probabilistic models.

🔗 Connect with Markus
👉 LinkedIn: https://www.linkedin.com/in/markus-boeck-aut/

Jürgen Cito (InferLog Holmes Co-Author)
Dr. Jürgen Cito is an Associate Professor for Computer Science at TU Wien (Vienna, Austria), where he leads the IPA Lab (Interactive Programming and Analysis Lab). His work sits at the intersection of Software Engineering (SE), Programming Languages (PL), and Artificial Intelligence (AI), with a specific focus on making software systems more reliable, explainable, and performant, and on empowering domain experts to leverage computational methods and AI in their own fields.

His research also maintains strong ties to industrial practice through industry engagements, including a Visiting Research Scientist position at Google (DevAI group) and a Software Engineer role at Meta (Probability group).

🔗 Connect with Jürgen
👉 LinkedIn: https://www.linkedin.com/in/jcito/

Christopher Fonnesbeck (Principal Data Scientist at PyMC Labs)
Chris is a Principal Quantitative Analyst at PyMC Labs and an Adjoint Associate Professor at the Vanderbilt University Medical Center, with 20 years of experience as a data scientist in academia, industry, and government, including 7 years in pro baseball research with the Philadelphia Phillies, New York Yankees, and Milwaukee Brewers. 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
👉 GitHub: https://github.com/fonnesbeck

Oriol Abril Pla (Principal Data Scientist at PyMC Labs)
Oriol discovered his passion for computational statistics and open source in 2018 during his MSc in Astrophysics and has been working the topic since then. He started contributing to ArviZ and PyMC in 2019, joining their core teams not long after that. He started in academia but he left after some years in order to be able to work more freely and collaboratively on open source, software and knowledge sharing. His main areas of interest are data visualization, model and inference diagnostics, model comparison, and prior elicitation. Within open source projects, he has also dedicated a large part of his work to documentation, governance and EDIA.

🔗 Connect with Oriol
👉 Linkedin: https://www.linkedin.com/in/oriol-abril-pla-1b9123180/https://www.linkedin.com/in/oriol-abril-pla
👉 GitHub: https://github.com/OriolAbril

💼 About the Host:
Evan Wimpey (Director of Analytics at PyMC Labs)
Evan helps clients design Bayesian solutions tailored to their goals, ensuring they understand both the how and why of inference. With master’s degrees in Economics and Analytics, he focuses on delivering clear value throughout projects and brings a unique twist with his background in data comedy.

🔗 Connect with Evan:
👉 Linkedin: https://www.linkedin.com/in/evan-wimpey/
👉 GitHub: https://github.com/ewimpey

📖 Code of Conduct:
Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs:
🌐 Website: https://www.pymc-labs.com/
👥 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/
🎮 Discord: https://discord.gg/KJt32Ty8

MMM Calibration: Engineering Business Impact through Bayesian Lift Integration

Wed, Apr 8 · 11:00 AM EDT

·

Online

Online

🎙️ Speakers: Dr. Juan Orduz, Benjamin Vincent, PhD,
Carlos Eduardo Trujillo Agostini, and Will Dean | ⏰ Time: 15:00 UTC / 8:00 AM PT / 11:00 AM ET / 5:00 PM Berlin

Your MMM drives multi-million dollar budget allocations, but can you prove its ROAS estimates are real? Uncalibrated models risk massive waste by ignoring the "ground truth" of experiments.

As leaders in Bayesian media measurement, PyMC Labs is at the forefront of engineering certainty into marketing analytics. Join Juan Orduz and our panel of experts for a deep dive into the technical framework and business impact of MMM calibration. We will move beyond theory to show you how to anchor your models in experimental truth, ensuring your media mix is a strategic growth engine rather than a "black box."

### What You Will Learn

By attending this webinar, you will gain a comprehensive understanding of how to bridge the gap between advanced Bayesian modeling and measurable ROI:

• Constraining ROAS with Prior Predictive Modeling: Learn how to use priors to keep your return on investment estimates within realistic, data-backed boundaries.
• The Power of ROAS Parameterization: Explore the practical applications of Google’s research on parameterization to improve model stability and accuracy.
• Calibration via Additional Likelihoods: Discover how to integrate external lift test data directly into your MMM to validate and refine your results.
• Advanced Geo-Level Calibration: A deep dive into using CausalPy for high-precision experiments at the geographic level.
• Overcoming Organizational Challenges: Understand the operational and strategic hurdles of implementing a calibrated measurement framework within your team.

📜 Outline of Talk / Agenda:

• 5 min: Introduction to PyMC Labs and speakers
• 40 min: Panel discussion
• 15 min: Q&A

-------------------------------------------------
💼 About the speakers:

Dr. Juan Orduz ( Principal Data Scientist at PyMC Labs)
Mathematician (Ph.D., Humboldt Universität zu Berlin) and data scientist with more than 8+ years of industry experience in the Tech sector. He is interested in interdisciplinary applications of mathematical methods, particularly time series analysis, Bayesian methods, and causal inference.

Connect with Dr. Juan: 👉 Linkedin 🧩 Github

Dr. Benjamin Vincent ( Principal Data Scientist at PyMC Labs)
Senior Data Scientist and Lead at PyMC Labs, where he specializes in Bayesian data analysis, causal inference, and decision science. With a background that bridges the gap between rigorous academic research and practical business solutions, Ben is a key contributor to the PyMC ecosystem.

Connect with Benjamin: 👉 LinkedIn🧩 Github

Carlos Eduardo Trujillo Agostini **(**Principal Data Scientist at PyMC Labs)
Carlos is a Marketing Scientist passionate about using data and AI to turn marketing strategy into measurable results. He’s worked with teams across Latin America, Europe, and Africa, including roles at Wise, Bolt, and Omnicom Media Group. As a core member of PyMC Labs, he contributes to open-source projects like PyMC-Marketing, blending statistical rigor with practical marketing insight.

Connect with Carlos: 👉 Linkedin 🧩 Github

Will Dean **(**Principal Data Scientist at PyMC Labs)
Statistician and Data Scientist specializing in geospatial and user analytics. With a deep interest in Bayesian methods, he focuses on transforming complex data into clear, meaningful insights through visualization. His work bridges analytics and software design, exploring how thoughtful engineering can make solving data challenges both simpler and more engaging.

Connect with Will 👉 Linkedin 🧩 Github
------------------------------------------------

📖 Code of Conduct:
Please note that participants are expected to abide by PyMC's Code of Conduct.

Connecting with PyMC Labs:
🌐 Website: https://www.pymc-labs.com/
👥 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/
🎮 Discord: https://discord.gg/9kuuqhBR