Covers: theory of Bayes' Theorem

- What is Bayes' Theorem and how can we implement it in our analyses?

The first half is a mathematical intro to Bayes, the latter half provides worked examples (math and code)

Jason Brownlee

Brandon Terrizzi**Total time needed: **~3 hours

- Learning Objectives
- With this list you have a gentle introduction to the intuitions and math underlying Bayes' Theorem and be able to apply this knowledge to interpreting statistical analyses and model fitting
- Potential Use Cases
- probability, statistical analyses, model fitting, machine learning
- Target Audience
- BEGINNERPython users, data scientists, data analysts

Go through the following **annotated items** *in order*:

ARTICLE 1. Joint, Marginal, and Conditional Probabilities

- What are the various types of probability and how do they relate to one another?

20 minutes

VIDEO 2. Conditional Probability

- What are conditional probabilities?

6 minutes

VIDEO 3. What is Bayes' Theorem

- What is Bayes' Theorem?

10 minutes

ARTICLE 4. Bayesian Probability in Machine Learning

- What is Bayes' Theorem and how can we implement it in our analyses?

60 minutes

ARTICLE 5. Bayesian vs. Frequentist Interpretation of Probability

- Hos is Bayes different from NHST?

60 minutes

Previewing stream ** Math and Foundations**

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