#PyMC

Registration is OPEN for Applied Bayesian Modeling with PyMC (Nov 2 to Dec 2)! Learn practical Bayesian statistics, hierarchical modeling, and uncertainty quantification live. Taught directly by the creators and maintainers of #PyMC. Sign up today:

If learning #Bayesian modeling was on your list for 2026, this is your last chance this year. Applied Bayesian Modeling with #PyMC, our final cohort of 2026, starts Monday, Nov 2: eight live sessions. Reserve your seat:

Applied #Bayesian Modeling starts Nov 2: four live weeks of Bayesian modeling with #PyMC, taught by the people who created PyMC and Stan and wrote Think Bayes. Instructor Evan Wimpey explains why the goal is a model you can understand and trust. Enroll:

Most rankings hand you an ordered list. Chris Fonnesbeck's new post asks how far apart two college football teams really are, and how sure we can be, using a Bayesian model in #PyMC that he tests on 929 games from 2025. Read the blog:

How to turn FDA guidance on Bayesian clinical trials into production Python code: Learn how dynamic borrowing in #PyMC lets 40 control patients carry the weight of ~177, featuring ESS metrics & model stress-testing. Walkthrough: #ClinicalTrials

We gave Claude a 15-page PyMC best-practices cheat sheet. Pass rates jumped from 60% → 93%. Hardest tasks saw the biggest gains: • Stochastic volatility: 0% → 67% • Runtime: 19 min → <3 min and more .. 👉Benchmark: #BayesianModeling #PyMC #AI

🚀 PyMC 5.10.4 has been officially landed on Colab! This latest version brings seamless integration of the numpyro sampler. Now, you can run blackjax and numpyro smoothly. Explore all the features and enhancements; check it out now on Colab: #PyMC

Linear regression assumes equal spacing. Multinomial ignores order entirely. In his latest tutorial, @AllenDowney breaks down how to model ordered survey responses properly using PyMC: #PyMC #DataScience #Python

We replaced a PyMC compiler with an LLM agent that transpiles to Rust. 3-7x faster sampling, validated against reference gradients, ~500 lines of scaffolding. Full technical dive by @twiecki : Open source: #PyMC #Rust #AgenticAI

1/5 🎉 Exciting news for Bayesian modeling enthusiasts! PyMC v5.3.0 has just been released with major changes, new features, bugfixes, and documentation improvements. #PyMC #DataScience #BayesianModeling #MachineLearning #PythonProgramming 🐍🧠

Everyone can do ML. Few can do Bayesian modeling. Set yourself apart with skills that drive deeper insight + better decisions. Join the next Applied Bayesian Modeling Workshop by PyMC Labs. Hands-on. Practical. Real-world. 📅 Limited seats → #PyMC

Major new BEAT release 2.0.0. is out! Finally, supporting python3.9+. #pymc #pyrocko #pytensor installation. New features: Multi-Sourcetype inference (e.g.Rectangular & MTSource) and boundary element modeling mode using GPU #cutde @tbenthompson. New tutorials coming. Stay tuned!

1/ Last semester, I taught a course on computational Bayesian inference in #pymc aimed at novice #Python-istas and budding #DataScience folk. Though not perfect, it is now complete and available for anyone, install-free: 🧵⤵️, some of my favorite parts:

Building a diffusion model for Simulation-Based Inference isn't one-size-fits-all. It's a true "garden of design choices." Join Stefan Radev, Thomas Wiecki, & Luca Fiaschi live Sept 8 to map these choices with BayesFlow + PyMC. Register: #PyMC #GenAI

👋Say goodbye to the hassle of deploying your #PyMC models! 🚀 The new #ModelBuilder class is here to simplify your workflow in deploying your PyMC models to production. Fit(), predict(), save(), and load() your PyMC models with ease!🤩 Check out now👉

We’re partnering with @hugobowne for a free live workshop! 🔥 Learn #Bayesian decision-making with #PyMC experts @AllenDowney & Christopher Fonnesbeck — from informative priors to rare-event prediction. 📅 Register now:

Migrating to PyMC v4.0? Check out this blog: • Quickest migration guide ever from PyMC3 v3 to PyMC v4.0 • by Eric J. Ma Eric shares a quick guide to help you migrate from PyMC v3 to PyMC v4 quickly and easily. #bayesian #statistics #pymc

⚖️ Bayesian methods give us a robust way to measure uncertainty 👀 By integrating them with genAI, we are building systems that go beyond simple predictions 🔔Stay tuned for deep-dives on Bayesian agents, causal AI & sound synthetic data: #genai #PyMC

Hockey goaltending stats are noisy. We used Bayesian spatial models to separate skill from luck, creating a fairer framework for scouting. See how Gaussian Processes clarify performance and improve decision-making. 👉 #SportsAnalytics #PyMC #Bayesian

Not a fan of #PyMC's `with pm.Model()` context API? Interesting new API idea for a function based API by @zaxtax. Implementation is just a few lines of code and it integrates seamlessly with the rest of the API.