Bayesian Modelling in Python
A Python tutorial and cookbook for implementing Bayesian modeling techniques using PyMC3.
What is Bayesian Modelling in Python?
Bayesian Modelling in Python is a tutorial and programming cookbook that teaches how to implement Bayesian modeling techniques using the PyMC3 library. It provides practical examples and Jupyter notebooks covering parameter estimation, model checking, hierarchical modeling, regression, survival analysis, and A/B testing. The project solves the need for accessible, code-focused resources that bridge Bayesian statistical theory with real-world Python implementation.
Data scientists, researchers, and developers who already understand Bayesian statistics fundamentals and want to learn practical implementation in Python using PyMC3.
Developers choose this tutorial because it offers a structured, hands-on approach with complete code examples, clear visualizations, and coverage of advanced topics like hierarchical modeling and survival analysis. It fills a gap between theoretical Bayesian statistics textbooks and applied programming guides.
Overview
A python tutorial on bayesian modeling techniques (PyMC3)
Use Cases
Best For
- Learning Bayesian modeling with PyMC3 through practical examples
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