Bayesian probability

Total time needed: ~4 hours
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
Go through the following annotated items in order:
ARTICLE 1. Bayes theorem
little advance explanation of Bayes theorem from Stanford university
30 minutes
VIDEO 2. Conditional probability
easy explanation of conditional probability as a prerequisite to bayesian statistic
12 minutes
ARTICLE 3. Bayesian statistic (MIT video)
Through video from MIT stats course
80 minutes
VIDEO 4. Bayes theorem simple explanation
a ​simple explanation of bayes theorem from Youtube video​
15 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 rule
30 minutes
ARTICLE 7. Bayesian statistics
Easy explanation from Wikipedia
30 minutes

Concepts Covered