Estimating Sloppy Directions via KDE: The Case of Kirman's Ants (opens in new tab)

Models whose predictions depend on only a handful of well-constrained parameter combinations, termed sloppy models, are ubiquitous in nonlinear stochastic systems. The information-geometric approach to sloppiness advocates using the symmetrized Kullback--Leibler divergence and its associated Hessian, the Fisher Information Matrix (FIM), as the natural loss function. However, prior applications have relied on analytically known or parametrically ...