Parameter estimation and uncertainty quantification using information geometry

TL;DR

This paper reviews likelihood-based parameter estimation and introduces information geometry techniques like geodesic curves and Riemann curvature to enhance uncertainty quantification and identifiability analysis.

Contribution

It integrates information geometry methods with traditional inference techniques to provide data-independent insights into uncertainty and identifiability.

Findings

Information geometry offers new insights into uncertainty quantification.

Geodesic curves and Riemann curvature inform data collection strategies.

Code implementation is available on GitHub.

Abstract

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.