Most investors assume the market is chaotic… unpredictable… impossible to “solve.” But what if that’s not quite true? In ...
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 ...
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. Join the global community of subscribers to PNAS, the flagship journal of ...
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, ...
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