Bayesian Interpretation of Probability

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

Concepts Covered