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

In biopharmaceutical manufacturing the interactions between cells, nutrients, and reagents in culture determine product quality. The big challenge for process developers is modeling these complex ...

A new technique can help researchers who use Bayesian inference achieve more accurate results more quickly, without a lot of additional work. Pollsters trying to predict presidential election results ...

How does Thermo Fisher Scientific's analytical software support the calculation of measurement uncertainty? Wilhelm Sanders: According to DIN EN ISO 17025, it is necessary to calculate the measurement ...

If you would like to learn more about the IAEA’s work, sign up for our weekly updates containing our most important news, multimedia and more. The IAEA facilitated the co-operation on research and ...

PV yield forecasts are widely considered to be inaccurate, partly because they underestimate uncertainty. Image: Tilt Renewables. The uncertainty in energy yield forecasts is frequently underestimated ...

Business schools and other professional programs teach powerful analytical methods for using information to make decisions. These methods are important and need to be learned. But what happens when ...