Data analysis & uncertainty

For simple measurements, mathematical models are usually straightforward, and it is clear how to analyse the data and calculate the uncertainty.

The BIPM Joint Committee on Guides for Metrology Working Group 1 (JCGM-WG1) has provided a large suite of guidance documents, including the well-known GUM, the Guide to the Expression of Uncertainty in Measurement. 


However, there are still many practical cases left in which it is not clear how the data can best be modeled and how to calculate the uncertainty. This can be due to a complex mix of systematic and random effects, unknown model errors, a very small or a very large amount of measurement data, hidden correlations or other complex structures in the measurement procedure or data model. Also, for regression and inverse problems optimal guidance is sometimes missing.

The Mathmet research topic ‘Data analysis and uncertainty’ will address these cases. Mathmet is actively engaging with stakeholders and collecting such research topics. These topics will be grouped and ranked before being added to the Mathmet Strategic Research Agenda.



Do you have a measurement science related need in this area? Or would you like to collaborate with Mathmet on a research topic in this area? Please don’t hesitate and contact us.

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