Open AccessParameter estimation and uncertainty quantification using information geometry
Author(s) -
John Sharp,
Alexander P Browning,
Kevin Burrage,
Matthew J. Simpson
Publication year - 2022
Publication title -
journal of the royal society interface
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.655
H-Index - 139
eISSN - 1742-5689
pISSN - 1742-5662
DOI - 10.1098/rsif.2021.0940
Subject(s) - geodesic , computer science , curvature , inference , information geometry , uncertainty quantification , bayesian probability , mathematics , algorithm , geometry , machine learning , artificial intelligence , scalar curvature
In this work, we: (i) review likelihood-based inference for parameter estimation and the construction of confidence regions; and (ii) explore the use of techniques from information geometry, including geodesic curves and Riemann scalar curvature, to supplement typical techniques for uncertainty quantification, such as Bayesian methods, profile likelihood, asymptotic analysis and bootstrapping. These techniques from information geometry provide data-independent insights into uncertainty and identifiability, and can be used to inform data collection decisions. All code used in this work to implement the inference and information geometry techniques is available onGitHub .