Continuous Random Variables

Continuous random variables can take on any value within an interval. They assume an uncountable number of possible values.

Example: Time measurements, heights, weights, temperatures, and distances.

In machine learning, continuous random variables are central to regression tasks, generative modeling, and any prediction involving real-valued outputs. Linear regression, neural networks with continuous outputs, and Gaussian processes all model relationships between continuous random variables.