The two main parts of this work are
- definition of appropriate data structures for different types of uncertainty analyses, and
- implementation of these data structures into analysis and visualization software for demonstration, testing, and use.
The different types of uncertainty analyses to be covered initially include analyses using the methods described in the JCGM Guide to the Expression of Uncertainty in Measurement (GUM), in Supplement 1 to the GUM (GS1), and Bayesian uncertainty assessments. All of these methods are based on the use of probabilistic models for the data and result in probability-based assessments of uncertainty such as confidence or credible intervals. After further evaluation, analogous data structures for methods based extensions of probability or on non-probabilistic methods also may be developed.
As data structures are developed, our goal is to add the capability for reading and writing these structures to one or more software packages for testing and demonstration of their use, as appropriate. The first analysis package into which we are incorporating the GUM and GS1 data structures is the NIST Uncertainty Machine, a free, web-based tool for the general assessment of measurement uncertainty using the GUM and GS1 methods. The figure below shows the NIST Uncertainty Machine’s input page.
The software currently only accepts configuration files using its own input format, but will be able to read and write MDCS structures soon as well.