A probability density function (PDF) describes the likelihood of different outcomes for a continuous random variable.

A random variable is one whose value is unknown or a function that assigns values to each of an experiment’s outcomes. A random variable can be discrete or continuous.

A discrete distribution is a statistical probability distribution that represents the possible discrete values a variable can take.

Kernel Density Estimation (KDE): A nonparametric method to estimate the probability density function of a random variable by averaging over locally weighted contributions of each data point.