Hands-on example: Bayesian linear regression; prior predictive checks; posterior sampling with NUTS; basic model diagnostics; posterior predictive checks.
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Speaker
Christopher Fonnesbeck
7
talks
Principal Quantitative Analyst
PyMC Labs; Adjunct Associate Professor, Vanderbilt University Medical Center
Christopher Fonnesbeck is a Principal Quantitative Analyst at PyMC Labs and Adjunct Associate Professor at Vanderbilt University Medical Center, with 20 years of experience as a data scientist in academia, industry, and government, and a focus on computational statistics, machine learning, Bayesian methods, and applied decision analysis.
Bio from: [Online] A Tutorial for Getting Started with PyMC
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Addressing frequently asked questions; debugging convergence issues; understanding and fixing divergences; performance optimization tips.
How AI/LLMs are changing PyMC workflows; PyMC's development roadmap; opportunities for contribution.
Recommended installation procedure; PyMC's computational backends; troubleshooting common installation issues; setting up development environments.
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.
Model contexts and random variables; prior and likelihood specification; working with observed data; PyMC's relationship with ArviZ.
ArviZ for visualization and diagnostics; related packages (Bambi, PyMC-experimental); finding and using PyMC example notebooks; community resources and support channels.