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.

翻译：在这项工作中,我们(1)审查参数估计和建立信任区域构建的基于概率的推论,(2)探索利用信息几何技术,包括大地曲线和里曼卡路曲曲线,以补充典型的不确定性量化技术,如巴耶斯方法、剖面概率、无症状分析和靴子穿透等,这些信息几何技术提供了数据独立的不确定性和可识别性方面的见解,可用于为数据收集决定提供信息。