Uncertainty and Sensitivity Analyses Methods for Agent-Based Mathematical Models: An Introductory Review
Methodology
2020-12-24 v3
Abstract
Multiscale, agent-based mathematical models of biological systems are often associated with model uncertainty and sensitivity to parameter perturbations. Here, three uncertainty and sensitivity analyses methods, that are suitable to use when working with agent-based models, are discussed. These methods are namely Consistency Analysis, Robustness Analysis and Latin Hypercube Analysis. This introductory review discusses origins, conventions, implementation and result interpretation of the aforementioned methods. Information on how to implement the discussed methods in MATLAB is included.
Cite
@article{arxiv.1911.08429,
title = {Uncertainty and Sensitivity Analyses Methods for Agent-Based Mathematical Models: An Introductory Review},
author = {Sara Hamis and Stanislav Stratiev and Gibin G Powathil},
journal= {arXiv preprint arXiv:1911.08429},
year = {2020}
}
Comments
41 pages