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 ...

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 ...

Objectives To (1) understand what behaviours, beliefs, demographics and structural factors predict US adults’ intention to get a COVID-19 vaccination, (2) identify segments of the population ...

Abstract: With its inherent causal reasoning and superior capacity for handling uncertainty, the belief rule base (BRB) has been widely applied in complex systems modeling. As a generalization of ...

I want to go back over something from Part 11, but in a more systematic and self-contained way. I’m stating these facts roughly now, to not get bogged down. But I’ll state them precisely, prove them, ...

A courseware module that covers the fundamental concepts in probability theory and their implications in data science. Topics include probability, random variables, and Bayes' Theorem.

In recent years, neural networks have once again triggered an increased interest among researchers in the machine learning community. So-called deep neural networks model functions using a composition ...

What Is A Probability Density Function? A probability density function, also known as a bell curve, is a fundamental statistics concept, that describes the likelihood of a continuous random variable ...

Quantum annealing (QA) can be competitive to classical algorithms in optimizing continuous-variable functions when running on appropriate hardware, show researchers from Tokyo Tech. By comparing the ...

The benchmark tests show that the noise-free realization of QA can significantly outperform state-of-the-art classical algorithms. Quantum annealing (QA) is a cutting-edge algorithm that leverages the ...