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- Learning Objectives
- Bayesian probability is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation representing a state of knowledge or as quantification of a personal belief.
- Potential Use Cases
- Bayes' theorem can be used to determine the accuracy of medical test results by taking into consideration how likely any given person is to have a disease and the general accuracy of the test.
- Target Audience
- INTERMEDIATE

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

ARTICLE 1. Bayes theorem

little advance explanation of Bayes theorem from Stanford university30 minutes

VIDEO 2. Conditional probability

easy explanation of conditional probability as a prerequisite to bayesian statistic12 minutes

ARTICLE 3. Bayesian statistic (MIT video)

Through video from MIT stats course80 minutes

VIDEO 4. Bayes theorem simple explanation

a simple explanation of bayes theorem from Youtube video15 minutes

ARTICLE 5. Prior distribution

it explains the different prior probabilities that can be used in Bayesian statistic 20 minutes

BOOK_CHAPTER 6. The Basics of Bayesian Statistics

It explains all required concepts:
- Bayesian vs. Frequentist Definitions of Probability
- Inference for a Proportion
- Bayes rule30 minutes

ARTICLE 7. Bayesian statistics

Easy explanation from Wikipedia 30 minutes

Previewing stream ** Math and Foundations**

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