Two Approaches to Calibration in Metrology
Conference
·
OSTI ID:1225332
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.
- Research Organization:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1225332
- Report Number(s):
- NREL/PR-5J00-65071
- Resource Relation:
- Conference: Conference on Uncertainty Quanitification;Savannah, GA;03/31/2014 - 04/03/2014
- Country of Publication:
- United States
- Language:
- English
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