Estimating global identifiability using conditional mutual information in a Bayesian framework.

ABSTRACT: A novel information-theoretic approach is proposed to assess the global practical identifiability of Bayesian statistical models. Based on the concept of conditional mutual information, an estimate of information gained for each model parameter is used to quantify the identifiability with practical considerations. No assumptions are made about the structure of the statistical model or the prior distribution while constructing the estimator. The estimator has the following notable advantages: first, no controlled experiment or data is required to conduct the practical identifiability analysis; second, unlike popular variance-based global sensitivity analysis methods, different forms of uncertainties, such as model-form, parameter, or measurement can be taken into account; third, the identif

SUBMITTER: Bhola S 

PROVIDER: S-EPMC10603099 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature