Chris Fonnesbeck
where else to find them
what they talk about
where they keep going back
- PyData London4 editions2019–2026
all 4 in the filters
on a stage with
- Bill Engelsonce
who talks about the same things
- Bill Engels2 talks
- Christopher Fonnesbeck2 talks
- Alexandre Andorra2 talks
- Thomas Wiecki7 talks
- Nicole Carlson6 talks
- Colin Carroll6 talks
| title | conference | date |
|---|---|---|
| Flexible Statistical Modeling | PyData London 2026 | 2026-06-05 |
Claims that BART gives calibrated probability intervals and that pymc-bart makes it practical get tested with concrete examples: F1 lap times and GSS… | ||
| Bayesian Time Series | PyData London 2025 | 2025-06-06 |
Practical checklist: tutorial uses PyMC in Jupyter (Conda / Pixie setups) to build AR/AR1/AR2, random‑walk and state‑space models in PyMC, add Fourier… | ||
| A Beginner's Guide to Variational Inference | PyData Virginia 2025 | 2025-04-18 |
Tutorial shows concrete claims and timings: PyMC runs ADVI and the newer Pathfinder and compares them against MCMC NUTS on real models. Example: Gaussian… | ||
| Making Gaussian Processes Useful | PyData Global 2024 | 2024-12-03 |
Tutorial turns Gaussian processes into a usable tool: hierarchical binomial and count models on baseball batting and slugging data, implemented in PyMC, use… | ||
| Probabilistic Python: An Introduction to Bayesian Modeling with PyMC | PyData London 2022 | 2022-06-17 |
Overview of what is actually done and what comes out: introduces PyMC v4 and the Aesara/JAX backends, fits models with the No-U-Turn Sampler (NUTS), and shows… | ||
| A Primer on Gaussian Processes for Regression Analysis | PyData New York City 2019 | 2019-11-04 |
Hands-on notebooks show how to specify, fit and validate Gaussian process regressions in Python using PyMC3 with gradient MCMC (NUTS/HMC) and occasional MAP… | ||
| An introduction to Markov Chain Monte Carlo using PyMC3 | PyData London 2019 | 2019-07-12 |
Covers concrete MCMC practice using PyMC3 and the repo https://github.com/fonnesbeck/mcmc_pydata_london_2019 with runnable notebooks: hand-implements… | ||
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