In this section we'll detail the prerequisites and course outline, listed below

• Prerequisites: 

  • Familiarity with PyData stack

  • Basics of probability

• Outline: 

  1. Motivation

  2. How it all fits together

  3. Primer on Probability Distributions

  4. Primer on Bayes Theorem

  5. Overview of Tools: NumPy, PyMC, ArviZ, Aesara

  6. Bayesian Linear Regression

  7. Samplers, Diagnostics, Convergences

  8. Hierarchical Models

  9. Bayesian Workflow