Machine Learning

A Gentle Introduction to Joint, Marginal, and Conditional Probability

Last Updated on May 6, 2020

Probability quantifies the uncertainty of the outcomes of a random variable.

It is relatively easy to understand and compute the probability for a single variable. Nevertheless, in machine learning, we often have many random variables that interact in often complex and unknown ways.

There are specific techniques that can be used to quantify the probability for multiple random variables, such as the joint, marginal, and conditional probability. These techniques provide the basis for a probabilistic understanding of fitting a predictive model to data.

In this post, you will discover a gentle introduction to joint, marginal, and conditional probability for multiple random variables.

After reading this post, you will know:

  • Joint probability is the probability of two events occurring simultaneously.
  • Marginal probability is the probability of an event irrespective of the outcome of another variable.
  • Conditional probability is the probability of one event occurring in the presence of a second event.

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.