Continuous Probability Distributions for Machine Learning

Last Updated on September 25, 2019

The probability for a continuous random variable can be summarized with a continuous probability distribution.

Continuous probability distributions are encountered in machine learning, most notably in the distribution of numerical input and output variables for models and in the distribution of errors made by models. Knowledge of the normal continuous probability distribution is also required more generally in the density and parameter estimation performed by many machine learning models.

As such, continuous probability distributions play an important role in applied machine learning and there are a few distributions that a practitioner must know about.

In this tutorial, you will discover continuous probability distributions used in machine learning.

After completing this tutorial, you will know:

• The probability of outcomes for continuous random variables can be summarized using continuous probability distributions.
• How to parametrize, define, and randomly sample from common continuous probability distributions.
• How to create probability density and cumulative density plots for common continuous probability distributions.

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