# Parameter estimation and uncertainty quantification using information geometry

arXiv: https://arxiv.org/abs/2111.12201 · HTML: https://mlsanity.com/paper/2111.12201/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2021.

## Built on by (most cited first, 1 of 1)

- [Conformal Prediction in Dynamic Biological Systems](https://mlsanity.com/paper/2409.02644/) (2024)

## Abstract (arXiv)

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.