Covers: theory of Bayes theorem
Estimated time needed to finish: 30 minutes
Why this is worth your time
little advance explanation of Bayes theorem from Stanford university
How to use this item?

Read whole article especially​ Conditional Probabilities and Bayes' Theorem in chapter 1

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Bayesian probability

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Total time needed: ~4 hours
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.
Who is This For ?
INTERMEDIATE
Click on each of the following annotated items to see details.
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

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