24 lines
928 B
Markdown
24 lines
928 B
Markdown
# Introductory Bayesian Inference
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## Module 1: Foundations
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### Descriptive Statistics
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- Objective: Explain mean, median, and variance.
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- Exercise: Summarize a small dataset.
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Descriptive Statistics introduces measures of center and spread.
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### Probability Basics
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- Objective: Explain conditional probability.
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- Exercise: Compute a simple conditional probability.
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Probability Basics introduces events, likelihood, and Bayes-style reasoning.
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## Module 2: Bayesian Updating
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### Prior and Posterior
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- Objective: Explain a prior distribution.
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- Objective: Explain how evidence changes belief.
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- Exercise: Compare prior and posterior beliefs.
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A Prior expresses assumptions before evidence. Posterior reasoning updates belief after evidence.
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### Capstone Mini Project
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- Exercise: Write a short project report comparing priors and posteriors.
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This project asks learners to critique assumptions and produce a small capstone artifact.
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