Bayesian uncertainty analysis represents a powerful statistical framework that integrates prior knowledge with observed measurement data to quantify uncertainty in a consistent probabilistic manner.

Researchers say that the methods used to evaluate AI systems’ capabilities routinely oversell AI performance and lack ...

The Heisenberg uncertainty principle, which has origins in physics, "states that there is a limit to the precision with which certain pairs of physical properties of a particle, such as position and ...

Assess a discrete measurement. Perform analyzes for potential and long term control and capability. Make decisions on measurement systems process improvement. In this module, we will learn to identify ...

This paper considers linear regression models when neither the response variable nor the covariates can be directly observed, but are measured with multiplicative distortion measurement errors. To ...

Through spectral analysis of long-term GNSS observations at the NanShan station, the researchers identified distinct annual and semi-annual cycles in ZTD variation, with greater delays in summer and ...

In recent years there has been an evolution in numerical models used to compute tsunami propagation and run-up. Many models currently available offer a wide array of choices to the users. In parallel ...

Multi-touch attribution (MTA) has officially gone the way of the dinosaur. As data privacy restrictions like Apple’s App Tracking Transparency (ATT) and Google’s elimination of cookies inhibit the ...

Scientists have achieved the most precise measurement to date of the mass of the W boson, one of nature's force-carrying particles. The measured value shows tension with the value expected based on ...