Toman, B.
and Elster, C.
(2011),
Bayesian Uncertainty Analysis for a Regression Model Versus Application of GUM Supplement 1 to the Least-Squares Estimate, Metrologia, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=907685
(Accessed December 13, 2024)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Bayesian Uncertainty Analysis for a Regression Model Versus Application of GUM Supplement 1 to the Least-Squares Estimate
Published
Author(s)
, Clemens Elster
Abstract
Application of least-squares as, for instance, in curve fitting is an important tool of data analysis in metrology. It is tempting to employ the supplement 1 to the GUM (GUM-S1) to evaluate the uncertainty associated with the resulting parameter estimates, albeit doing so is beyond the specified scope of GUM-S1. We compare the result of such a procedure to a Bayesian uncertainty analysis of the corresponding regression model. It is shown that under certain assumptions both analyses yield the same results but this is not true in general. Some simple examples are given which illustrate the similarities and differences between the two approaches.Citation
Metrologia
Pub Type
Journals
Download Paper
Keywords
Monte Carlo uncertainty analysis, GUM Supplement 1, Bayesian analysis, least squares estimation.
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.
Created May 5, 2011, Updated February 19, 2017