Covers: application of Conditional Probability
Estimated time needed to finish: 6 minutes
Questions this item addresses:
  • What are conditional probabilities?
How to use this item?

this is short, watch the entire video

Author(s) / creator(s) / reference(s)
Trevor Bazzet
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Bayesian Interpretation of Probability

Contributors
Total time needed: ~3 hours
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
Who is This For ?
BEGINNERPython users, data scientists, data analysts
Click on each of the following annotated items to see details.
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

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