Didactopus/examples/sample_course.md

Introductory Bayesian Inference

Module 1: Foundations

Descriptive Statistics

• Objective: Explain mean, median, and variance.
• Exercise: Summarize a small dataset. Descriptive Statistics introduces measures of center and spread.

Probability Basics

• Objective: Explain conditional probability.
• Exercise: Compute a simple conditional probability. Probability Basics introduces events, likelihood, and Bayes-style reasoning.

Module 2: Bayesian Updating

Prior and Posterior

• Objective: Explain a prior distribution.
• Objective: Explain how evidence changes belief.
• Exercise: Compare prior and posterior beliefs. A Prior expresses assumptions before evidence. Posterior reasoning updates belief after evidence.

Capstone Mini Project

• Exercise: Write a short project report comparing priors and posteriors. This project asks learners to critique assumptions and produce a small capstone artifact.