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.

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 ...

Part-I, Probability (Chapters 1 – 3), lays a solid groundwork for probability theory, and introduces applications in counting, gambling, reliability, and security. Part-II, Random Variables (Chapters ...

Moments of continuous random variables admitting a probability density function are studied. We show that, under certain assumptions, the moments of a random variable can be characterized in terms of ...

A probability distribution, usually displayed graphically, shows the relative likelihood of all possible outcomes occurring within a specific time period.

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 ...