Neural parameter calibration for large-scale multiagent models.

ABSTRACT: Computational models have become a powerful tool in the quantitative sciences to understand the behavior of complex systems that evolve in time. However, they often contain a potentially large number of free parameters whose values cannot be obtained from theory but need to be inferred from data. This is especially the case for models in the social sciences, economics, or computational epidemiology. Yet, many current parameter estimation methods are mathematically involved and computationally slow to run. In this paper, we present a computationally simple and fast method to retrieve accurate probability densities for model parameters using neural differential equations. We present a pipeline comprising multiagent models acting as forward solvers for systems of ordinary or stochastic differ

SUBMITTER: Gaskin T 

PROVIDER: S-EPMC9963791 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature