Abstract: Traditionally, the uncertainty qualification is utilized with the known probability distribution function (PDF). However, in some scenarios, the PDFs of some uncertain variables are modeled ...

A simple random sample is a subset of a statistical population where each member of the population is equally likely to be ...

First formulated in the late 19th century by Austrian physicist and mathematician Ludwig Boltzmann, this principle remains ...

Uncover 25 cool random facts most people go their whole lives without knowing! Explore mind-bending truths about science, ...

Abstract: In probability theory, variance is a device to measure dispersion of random variable from its mean. In order to explain complex uncertain random variable,variance and pseudo-variance are ...

This investigation was supported (in part) by the U.S. Public Health Service, National Institutes of Health, grant GM-10525. Note: The article usage is presented with a three- to four-day delay and ...

Probability theory is indispensable in computer science: It is at the core of artificial intelligence and machine learning, which require decision making under uncertainty. It is integral to CS theory ...

ABSTRACT: Repeated convolution and truncation of a truncated fat-tailed distribution, instead of Monte Carlo simulation, for pricing a discrete, simple barrier option is presented. The parameters for ...

A discrete random variable is a type of random variable that can take on a countable set of distinct values. Common examples include the number of children in a family, the outcome of rolling a die, ...