By Duncan Burt, Chief Strategic Growth Officer, Reactive Technologies Sponsored content. The shape of electricity demand is ...

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

Currently hierarchical data models (HDM) must be generated with the same EDA tool that customers will use to consume the HDM ...

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 ...

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 ...

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 ...

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 ...