Two Approaches to Calibration in Metrology: NREL (National Renewable Energy Laboratory)

Research output: NRELPresentation


Inferring mathematical relationships with quantified uncertainty from measurement data is common to computational science and metrology. Sufficient knowledge of measurement process noise enables Bayesian inference. Otherwise, an alternative approach is required, here termed compartmentalized inference, because collection of uncertain data and model inference occur independently. Bayesian parameterized model inference is compared to a Bayesian-compatible compartmentalized approach for ISO-GUM compliant calibration problems in renewable energy metrology. In either approach, model evidence can help reduce model discrepancy.
Original languageAmerican English
Number of pages24
StatePublished - 2014

Publication series

NamePresented at the Society for Industrial and Applied Mathematics (SIAM) Conference on Uncertainty Quantification, 31 March - 3 April 2014, Savannah, Georgia

NREL Publication Number

  • NREL/PR-5J00-65071


  • calibrations
  • computational science
  • metrology


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