Strategic model reduction by analysing model sloppiness: a case study in coral calcification

04/12/2022
by   Sarah A. Vollert, et al.
0

It can be difficult to identify ways to reduce the complexity of large models whilst maintaining predictive power, particularly where there are hidden parameter interdependencies. Here, we demonstrate that the analysis of model sloppiness can be a new invaluable tool for strategically simplifying complex models. Such an analysis identifies parameter combinations which strongly and/or weakly inform model behaviours, yet the approach has not previously been used to inform model reduction. Using a case study on a coral calcification model calibrated to experimental data, we show how the analysis of model sloppiness can strategically inform model simplifications which maintain predictive power. Additionally, when comparing various approaches to analysing sloppiness, we find that Bayesian methods can be advantageous when unambiguous identification of the best-fit model parameters is a challenge for standard optimisation procedures.

READ FULL TEXT

page 11

page 13

research
03/29/2022

Analysis of sloppiness in model simulations: unveiling parameter uncertainty when mathematical models are fitted to data

This work introduces a Bayesian approach to assess the sensitivity of mo...
research
01/29/2020

Reducing complexity and unidentifiability when modelling human atrial cells

Mathematical models of a cellular action potential in cardiac modelling ...
research
10/08/2019

Percentile-Based Residuals for Model Assessment

Residuals are a key component of diagnosing model fit. The usual practic...
research
10/28/2020

Three Applications of Entropy to Gerrymandering

This preprint is an exploration in how a single mathematical idea - entr...
research
05/02/2022

A Case Study on Parallel HDF5 Dataset Concatenation for High Energy Physics Data Analysis

In High Energy Physics (HEP), experimentalists generate large volumes of...
research
06/06/2018

Check yourself before you wreck yourself: Assessing discrete choice models through predictive simulations

Typically, discrete choice modelers develop ever-more advanced models an...
research
02/16/2022

Where the Model Frequently Meets the Road: Combining Statistical, Formal, and Case Study Methods

This paper analyzes the working or default assumptions researchers in th...

Please sign up or login with your details

Forgot password? Click here to reset