# 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.