Year: 2025
Type: article
Abstract: Density estimation neural networks (DENNs) represent a form of artificial neural network designed to provide an efficient approach to the Bayesian estimation of a probability density on a model parameter space, conditioned on an empirical observation of the underlying system. Despite their efficiency and potential, DENNs remain underutilized for parameter estimation in mathematical modeling. In this work, we aim to boost the accessibility of the DENN approach by providing a user-friendly introduction and code that makes it easy for users to harness existing, cutting-edge DENN software. Furthermore, we insert an easily-implemented preliminary data simulation step that reduces the computational demands of the approach and empirically demonstrates that it maintains the accuracy of parameter estimation for a stochastic oscillator model.
DOI: 10.3934/mbe.2025116
Authors: Bosi Hou, Jonathan E. Rubin
Institutions: Columbia University, University of Pittsburgh
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,000
Funder: National Science Foundation
OpenAlex ID: W4415787549