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Title: Two Approaches to Calibration in Metrology

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.
Authors:
Publication Date:
OSTI Identifier:
1225332
Report Number(s):
NREL/PR-5J00-65071
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Conference on Uncertainty Quanitification;Savannah, GA;03/31/2014 - 04/03/2014
Research Org:
NREL (National Renewable Energy Laboratory (NREL)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 47 OTHER INSTRUMENTATION Metrology; Calibration; Computational science