Model tuning in engineering: Uncovering the logic

Citation data:

The Journal of Strain Analysis for Engineering Design, ISSN: 0309-3247, Vol: 51, Issue: 1, Page: 63-71

Publication Year:
2016
Usage 121
Downloads 118
Abstract Views 3
Social Media 5
Tweets 5
Citations 2
Citation Indexes 2
Repository URL:
http://philsci-archive.pitt.edu/id/eprint/11475
DOI:
10.1177/0309324715575445
Author(s):
Steele, Katie, Werndl, Charlotte
Publisher(s):
SAGE Publications
Tags:
Engineering, Mathematics
Most Recent Tweet View All Tweets
article description
In engineering, as in other scientific fields, researchers seek to confirm their models with real-world data. It is common practice to assess models in terms of the distance between the model outputs and the corresponding experimental observations. An important question that arises is whether the model should then be 'tuned', in the sense of estimating the values of free parameters to get a better fit with the data, and furthermore whether the tuned model can be confirmed with the same data used to tune it. This dual use of data is often disparagingly referred to as 'double-counting'. Here, we analyse these issues, with reference to selected research articles in engineering (one mechanical and the other civil). Our example studies illustrate more and less controversial practices of model tuning and double-counting, both of which, we argue, can be shown to be legitimate within a Bayesian framework. The question nonetheless remains as to whether the implied scientific assumptions in each case are apt from the engineering point of view.

This article has 0 Wikipedia mention.