Continuous Distribution

Continuous distribution refers to a type of probability distribution that describes the likelihood of outcomes for continuous random variables, which can take any real numerical value within a given range. Unlike discrete distributions, where outcomes are distinct and countable, continuous distributions represent probabilities through a probability density function (PDF). This function indicates the relative likelihood of a variable falling within a specific interval. Key examples of continuous distributions include the normal, exponential, and uniform distributions, each playing a crucial role in statistical analysis and machine learning applications, particularly in modeling numerical data and understanding variability in predictions.