Parameter estimation and uncertainty quantification using information geometry

JA Sharp, AP Browning, K Burrage, MJ Simpson

Preprint

In this work we (1) review likelihood-based inference for parameter estimation and the construction of confidence regions, and (2) 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 on GitHub.