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Learn Probability

These marimo notebooks teach the fundamentals of probability with an emphasis on interactive learning and computation in Python.

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You can open and run these notebooks in molab, marimo's free hosted notebook platform.

Much of the structure and many explanations are adapted from Chris Piech's Probability for Computer Scientists course reader.

Notebooks

  • Open in molab Sets
  • Open in molab Axioms of Probability
  • Open in molab Probability of Or
  • Open in molab Conditional Probability
  • Open in molab Independence in Probability Theory
  • Open in molab Probability of And
  • Open in molab Law of Total Probability
  • Open in molab Bayes' Theorem
  • Open in molab Random Variables
  • Open in molab Probability Mass Functions
  • Open in molab Expectation
  • Open in molab Variance
  • Open in molab Bernoulli Distribution
  • Open in molab Binomial Distribution
  • Open in molab Poisson Distribution
  • Open in molab Continuous Distributions
  • Open in molab Normal Distribution
  • Open in molab Central Limit Theorem
  • Open in molab Maximum Likelihood Estimation
  • Open in molab Naive Bayes Classification
  • Open in molab Logistic Regression

Contributors

Thanks to our notebook authors:

Running Notebooks

To run a notebook locally, use:

uvx marimo edit <URL>

You can also open notebooks in our online playground by adding marimo.app/ to a notebook's URL.

Want to Contribute?

Help us improve these learning materials by contributing to the GitHub repository. We welcome new content, bug fixes, and improvements!

Contribute on GitHub