Neural Networks for Parameter Estimation in Intractable Models

TL;DR

This paper introduces a deep learning approach to estimate parameters in complex statistical models where traditional methods are computationally prohibitive, demonstrating improved accuracy and efficiency.

Contribution

It presents a novel neural network-based method for parameter estimation in intractable models, especially max-stable processes, outperforming existing techniques.

Findings

Neural networks achieve high accuracy in parameter estimation.

The method significantly reduces computational time.

Applicable to other complex statistical models.

Abstract

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.