Eigenvalue Analysis via Kernel Density Estimation

TL;DR

This paper introduces a novel eigenvalue sensitivity analysis method for system dynamics models using mutual information estimated through kernel density estimation, offering a new approach to multivariate eigenvalue analysis.

Contribution

The paper presents a new multivariate eigenvalue sensitivity analysis method based on mutual information and kernel density estimation, which is both novel and efficient.

Findings

Demonstrates the effectiveness of the proposed method

Provides a new perspective on eigenvalue sensitivity analysis

Shows potential for improved analysis of system dynamics

Abstract

In this paper, we propose an eigenvalue analysis -- of system dynamics models -- based on the Mutual Information measure, which in turn will be estimated via the Kernel Density Estimation method. We postulate that the proposed approach represents a novel and efficient multivariate eigenvalue sensitivity analysis.