6 citations · 6 across the 2 of their papers we have counts for
5 papers
Nonstationary seasonal model for daily mean temperature distribution bridging bulk and tails
Mitchell Krock, Julie Bessac, Michael L. Stein +1
In traditional extreme value analysis, the bulk of the data is ignored, and only the tails of the distribution are used for inference. Extreme observations are specified as values…
Neural Networks for Parameter Estimation in Intractable Models
Amanda Lenzi, Julie Bessac, Johann Rudi +1
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 par…
SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors
Kai Zhao, Sheng Di, Xin Liang +5
Efficient error-controlled lossy compressors are becoming critical to the success of today's large-scale scientific applications because of the ever-increasing volume of data produ…
Parameter Estimation with Dense and Convolutional Neural Networks Applied to the FitzHugh-Nagumo ODE
Johann Rudi, Julie Bessac, Amanda Lenzi
Machine learning algorithms have been successfully used to approximate nonlinear maps under weak assumptions on the structure and properties of the maps. We present deep neural net…
Statistical Treatment of Inverse Problems Constrained by Differential Equations-Based Models with Stochastic Terms
Emil M. Constantinescu, Noemi Petra, Julie Bessac +1
This paper introduces a statistical treatment of inverse problems constrained by models with stochastic terms. The solution of the forward problem is given by a distribution repres…