Machine Learning

How to Develop an Intuition for Joint, Marginal, and Conditional Probability

Last Updated on December 6, 2019

Probability for a single random variable is straight forward, although it can become complicated when considering two or more variables.

With just two variables, we may be interested in the probability of two simultaneous events, called joint probability: the probability of one event given the occurrence of another event called the conditional probability, or just the probability of an event regardless of other variables, called the marginal probability.

These types of probability are easy to define but the intuition behind their meaning can take some time to sink in, requiring some worked examples that can be tinkered with.

In this tutorial, you will discover the intuitions behind calculating the joint, marginal, and conditional probability.

After completing this tutorial, you will know:

  • How to calculate joint, marginal, and conditional probability for independent random variables.
  • How to collect observations from joint random variables and construct a joint probability table.
  • How to calculate joint, marginal, and conditional probability from a joint probability table.

Kick-start your project with my new book Probability for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.