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 ...

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.

Given two discrete random variables X and Y, an operational approach is undertaken to quantify the “leakage” of information from X to Y. The resulting measure ℒ(X→Y ) is called maximal leakage, and is ...

Definition 19 The Cauchy-Schwartz mutual information ICS (X; Y) between two discrete random variables X and Y, with a joint probability mass function xy (x, y) and marginal probability mass functions ...

This project aims to measure the credit risk of LendingClub, (an American peer-to-peer lending company), by calculating the expected loss of their outstanding loans. Credit risk is the likelihood that ...

For instance, if there are two discrete random variables X and Y with different possible values (x1, x2) and (y1, y2) respectively, create a joint probability table listing all combinations: ...

Spread the loveIn the field of statistics and probability, marginal distribution plays a critical role in understanding the behavior of variables. It is a method used to determine the probability ...

Learning the joint probability of random variables (RVs) is the cornerstone of statistical signal processing and machine learning. However, direct nonparametric estimation for high-dimensional joint ...

Discrete distributions: Examples include the binomial distribution, Poisson distribution, and geometric distribution, which are used for discrete random variables.

Example of a discrete joint probability distribution Consider two discrete random variables, ( X ) and ( Y ), representing the outcomes of rolling two dice.