Neural Networks for Parameter Estimation in Intractable Models
This provides a competitive alternative for statisticians and researchers dealing with computationally infeasible models, though it is incremental as it adapts existing deep learning methods to a specific domain.
The paper tackles parameter estimation in intractable statistical models like max-stable processes by using deep neural networks trained on simulation data, achieving significant improvements in accuracy and computational time.
We propose to use deep learning to estimate parameters in statistical models when standard likelihood estimation methods are computationally infeasible. We show how to estimate parameters from max-stable processes, where inference is exceptionally challenging even with small datasets but simulation is straightforward. We use data from model simulations as input and train deep neural networks to learn statistical parameters. Our neural-network-based method provides a competitive alternative to current approaches, as demonstrated by considerable accuracy and computational time improvements. It serves as a proof of concept for deep learning in statistical parameter estimation and can be extended to other estimation problems.