mc-ve-informative-prior

This repository contains the R code corresponding to the article titled "Uncertainty evaluation using virtual experiments: bridging JCGM 101 and a Bayesian framework", which is currently under review. The synthetic example demonstrates the implementation of a standard JCGM 101 method of uncertainty evaluation and a Monte Carlo sampling approach involving a virtual experiment (the MC-VE approach). Furthermore, both methods are repeated incorporating an informative prior. This repository also contains the Python implementation for the informative MC-VE approach that can be applied to a wide class of non-linear virtual experiments. The informative approach allows for the explicit incorporation of prior knowledge about the variability of the measurement device used to obtain the real measurements.

Support

For general questions, please contact finn.hughes@ptb.de

Disclaimer

This software was developed at Physikalisch-Technische Bundesanstalt (PTB). The software is made available "as is" free of cost. PTB assumes no responsibility whatsoever for its use by other parties, and makes no guarantees, expressed or implied, about its quality, reliability, safety, suitability or any other characteristic. In no event will PTB be liable for any direct, indirect or consequential damage arising in connection.

R version and packages

Developed and tested with R version 4.4.1. No additional packages are required.

Python instance and packages

Developed and tested with Python == 3.13.0. Further packages defined in requirements.txt.