So far, you learned about discrete random variables and how to calculate or visualize their distribution functions. In this lesson, you'll learn about continuous variables and probability density ...

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.

Discrete and continuous probability distributions are two fundamental types of probability distributions, each describing different kinds of random variables.

This table represents a discrete probability function, which shows the probability associated with each possible value of a discrete random variable. Such distributions can also be displayed ...

Probability Distributions with R This repository contains R code for developing and analyzing probability distributions for both discrete and continuous variables.

Jensen gave a lower bound to Eρ (T), where ρ is a convex function of the random vector T. Madansky has obtained an upper bound via the theory of moment spaces of multivariate distributions. In ...

Books Received Published: 01 January 1938 (1) Généralités sur les probabilités; variables aléatoires (2) Théorie de l'addition des variables aléatoires (3) Random Variables and Probability ...

Let {Xk, k ≥ 1} be a sequence of independent, identically distributed nonnegative random variables with common distribution function F and finite expectation $\mu > 0$. Under the assumption that the ...