PhilSci Archive

Model tuning in engineering: Uncovering the logic

Steele, Katie and Werndl, Charlotte (2015) Model tuning in engineering: Uncovering the logic. [Preprint]

[img] PDF
JSAReviseFinal.pdf

Download (219kB)

Abstract

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 papers in engineering (one mechanical, 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.


Export/Citation: EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL
Social Networking:
Share |

Item Type: Preprint
Creators:
CreatorsEmailORCID
Steele, Katie
Werndl, Charlotte
Keywords: tuning, calibration, confirmation, double-counting, evidence, engineering
Subjects: General Issues > Confirmation/Induction
Specific Sciences > Probability/Statistics
Depositing User: Charlotte Werndl
Date Deposited: 26 May 2015 15:07
Last Modified: 26 May 2015 15:07
Item ID: 11475
Official URL: http://sdj.sagepub.com/content/early/2015/03/20/03...
DOI or Unique Handle: 10.1177/0309324715575445
Subjects: General Issues > Confirmation/Induction
Specific Sciences > Probability/Statistics
Date: 2015
URI: http://philsci-archive.pitt.edu/id/eprint/11475

Monthly Views for the past 3 years

Monthly Downloads for the past 3 years

Plum Analytics

Altmetric.com

Actions (login required)

View Item View Item