AllenDowney/BayesMadeSimple

Code for a tutorial on Bayesian Statistics by Allen Downey.

What it solves

This project addresses the lack of beginner-friendly resources for learning Bayesian statistics. It helps programmers leverage their existing Python skills to understand statistical concepts without requiring a prior background in the field.

How it works

The tutorial uses grid algorithms to introduce Bayesian methods, which helps build a conceptual foundation before moving on to more complex techniques like MCMC. It consists of three interactive Jupyter notebooks that cover practical problems, such as estimating probabilities and making decisions under uncertainty, using libraries like pandas and NumPy.

Who it’s for

This is designed for beginners who are comfortable with basic Python programming. While some familiarity with NumPy and pandas is helpful, no prior statistical knowledge is required.

Highlights

  • Hands-on learning through three specific modules: The Cookie Problem, The Euro Problem, and The Bayesian Bandit.
  • Ready-to-use notebooks available via Google Colab, requiring no local installation.
  • Includes both exercise notebooks and completed solution versions for self-paced practice.
  • Provides a clear path for further study, including links to the author's comprehensive book, Think Bayes.

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