Neural Networks for Parameter Estimation in Intractable Models

Understand

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.