STAMPS@CMU presents:

Can Neural Networks be used for Parameter Estimation?

by Amanda Lenzi (Argonne National Laboratory)

Online webinar January 21, 2022 at 1:30-2:30 PM ET.
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Neural networks have proved successful in various applications in approximating nonlinear maps based on training datasets. Can they also be used to estimate parameters in statistical models when the standard likelihood estimation or Bayesian methods are not (computationally) feasible? In this talk, I will discuss this topic towards the aim of estimating parameters from a model for multivariate extremes, where inference is exceptionally challenging, but simulation from the model is easy and fast. I will demonstrate that in this example, neural networks can provide a competitive alternative to current approaches, with considerable improvements in accuracy and computational time. A key ingredient for this result is to actively use our statistical knowledge about parameters and data to make the problem more palatable for the neural network.


Amanda Lenzi is Postdoctoral Appointee at Argonne National Laboratory. She was a Postdoctoral Fellow at King Abdullah School of Science and Technology (KAUST) before coming to Argonne. She obtained her PhD degree in Statistics from the Technical University of Denmark in 2017 and her BS and MS degrees at the University of Campinas, São Paulo, Brazil. Her main research interests concern statistical modeling, prediction, simulation, and uncertainty quantification of spatiotemporal data from applications relating to energy as well as environmental science. She is also interested in computational methods for large datasets and the use of machine learning to improve the modeling of these complex spatiotemporal processes.