DeepAI AI Chat
Log In Sign Up

On structural and practical identifiability

02/09/2021
by   Franz-Georg Wieland, et al.
0

We discuss issues of structural and practical identifiability of partially observed differential equations which are often applied in systems biology. The development of mathematical methods to investigate structural non-identifiability has a long tradition. Computationally efficient methods to detect and cure it have been developed recently. Practical non-identifiability on the other hand has not been investigated at the same conceptually clear level. We argue that practical identifiability is more challenging than structural identifiability when it comes to modelling experimental data. We discuss that the classical approach based on the Fisher information matrix has severe shortcomings. As an alternative, we propose using the profile likelihood, which is a powerful approach to detect and resolve practical non-identifiability.

READ FULL TEXT
10/06/2017

Empirical Likelihood for Linear Structural Equation Models with Dependent Errors

We consider linear structural equation models that are associated with m...
08/20/2020

Varying-coefficient stochastic differential equations with applications in ecology

Stochastic differential equations (SDEs) are popular tools to analyse ti...
11/16/2021

Implicit Method for Degenerated Differential-Algebraic Equations and Applications

Systems of differential-algebraic equations are routinely automatically ...
06/04/2020

Learning DAGs without imposing acyclicity

We explore if it is possible to learn a directed acyclic graph (DAG) fro...
01/19/2023

The Lost Art of Mathematical Modelling

We provide a critique of mathematical biology in light of rapid developm...
03/20/2021

Understanding Loss Landscapes of Neural Network Models in Solving Partial Differential Equations

Solving partial differential equations (PDEs) by parametrizing its solut...