3Blue1Brown explores perhaps the most important formula in probability: Bayes Theorem.

The study with Steve:

Learn about bayesian deep learning and how to apply it to a quantum computer from Alex Pozas-Kerstjens

You’ll hear the term Bayes or Bayesian come up a lot in data science, but this video explores the theory with tennis balls and a table.

Here’s a great exploration of Bayes’ Theorem and how to use it in real world problems.

Bayes’ theorem is a way to figure out conditional probability. Conditional probability is the probability of an event happening, given that it has some relationship to one or more other events. For example, your probability of getting a parking space is connected to the time of day you park, where you park, and what conventions are going on at any time. Bayes’ theorem is slightly more nuanced. In a nutshell, it gives you the actual probability of an event given information about tests.

In case my previous post had left you wanting to know more about Bayes’ Theorem.

With apologies to Meghan Trainor.

Here’s a great introduction to Bayes Theorem and Hidden Markov Models, with simple examples. If you understand basic probability, then you can follow along.

The great Brandon Rohrer explains how Bayes’ Theorem works in this video. For another look at Bayesian thinking, check out Julia Galef and some of the posts I’ve written featuring her videos.

Julia Galef outlines the most important principles of thinking like a Bayesian.

Julia Galef uses pictures to illustrate the mechanics of “Bayes’ rule,” a mathematical theorem about how to update your beliefs as you encounter new evidence.