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

Probability Density Function Calculating probabilities for continuous random variables requires a different approach from the methods used with discrete variables. If all the outcomes of a continuous ...

A continuous bivariate joint density function defines the probability distribution for a pair of random variables. For example, the function f (x,y) = 1 when both x and y are in the interval [0,1] and ...

The problem considered here is the estimation of the probability density function f (x1, ⋯, xp) at a point z = (z1, ⋯, zp) where f is positive and continuous. An estimator is proposed and consistency ...

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.

Fundamental methods are developed for the derivation of the probability density function and moments of rational algebraic functions of independent random variables. Laplace and Mellin integral ...

Recent investigations have shown that a bivariant lognormal probability density function predicts the statistical moments and correlations of adult human height and weight so extensively and closely ...

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