Information sensitivity functions to assess parameter information gain and identifiability of dynamical systems

TL;DR

The paper introduces Information Sensitivity Functions (ISFs), a novel, computationally simple method based on sensitivity analysis and information theory, to evaluate parameter information gain and identifiability in dynamical systems.

Contribution

It presents ISFs as an easy-to-compute tool that combines sensitivity analysis with Bayesian and information-theoretic measures to assess parameter identifiability and correlations.

Findings

ISFs quantify information gain about parameters over time.

They help identify regions with high parameter information.

They assess the impact of noise and experimental design on parameter identifiability.

Abstract

A new class of functions, called the `Information sensitivity functions' (ISFs), which quantify the information gain about the parameters through the measurements/observables of a dynamical system are presented. These functions can be easily computed through classical sensitivity functions alone and are based on Bayesian and information-theoretic approaches. While marginal information gain is quantified by decrease in differential entropy, correlations between arbitrary sets of parameters are assessed through mutual information. For individual parameters these information gains are also presented as marginal posterior variances, and, to assess the effect of correlations, as conditional variances when other parameters are given. The easy to interpret ISFs can be used to a) identify time-intervals or regions in dynamical system behaviour where information about the parameters is concentrated; b) assess the effect of measurement noise on the information gain for the parameters; c) assess whether sufficient information in an experimental protocol (input, measurements, and their frequency) is available to identify the parameters; d) assess correlation in the posterior distribution of the parameters to identify the sets of parameters that are likely to be indistinguishable; and e) assess identifiability problems for particular sets of parameters.