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

Single-cell omics is a transformative area within life sciences, enabling researchers to untangle cellular heterogeneity, uncover developmental lineages, ...

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 ...

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 ...

Suggested Citation: "Appendix B: Types of Uncertainty." National Academies of Sciences, Engineering, and Medicine. 2023. Risk Analysis Methods for Nuclear War and Nuclear Terrorism. Washington, DC: ...

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 ...

The BBVA Foundation has awarded the Frontiers of Knowledge Award in Economics, Finance and Management to Charles Manski ...