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
- Implementing hierarchical Bayesian models for pooled data analysis
- Applying Bayesian regression techniques to count data using Poisson models
- Conducting Bayesian A/B testing for proportions and means
- Performing Bayesian survival analysis with hazard models
- Comparing frequentist and Bayesian approaches to parameter estimation
Not Ideal For
- Individuals with zero background in Bayesian statistics seeking an introductory theoretical foundation
- Teams needing production-ready, optimized Bayesian model code without educational narratives
- Learners who prefer interactive or video-based tutorials over static Jupyter notebooks
- Researchers requiring comprehensive coverage of Bayesian methods across multiple libraries beyond PyMC3
Pros & Cons
Pros
Emphasizes hands-on application with executable Jupyter notebooks, inspired by resources like 'Bayesian Methods for Hackers', as stated in the philosophy.
Provides a clear progression from basic parameter estimation to advanced topics like survival analysis, detailed in the contents section.
Uses visual styles adopted from BMH to illustrate complex concepts, making the learning process more intuitive.
Includes practical examples for hierarchical modeling and Bayesian A/B testing, which are valuable for applied data science.
Cons
The README explicitly targets those who understand Bayesian fundamentals, leaving beginners without theoretical background behind.
Actively being worked on with contributions welcomed for areas like survival analysis, indicating potential gaps in content.
Solely focuses on PyMC3, which may not align with projects using alternative Bayesian inference tools like Stan or Pyro.
Frequently Asked Questions
Related Projects
PyMCBayesian Modeling and Probabilistic Programming in Python
19 QuestionsA machine learning / bayesian inference engine assigning attributes to objects
Time Series Forecasting with Bayesian Modeling by Michael Grogan, a 5-project series - paid but the first project is free
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