Neural Networks for Parameter Estimation in Intractable Models

29 July 2021

Papers citing "Neural Networks for Parameter Estimation in Intractable Models"

21 / 21 papers shown

Title
LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data Antony Sikorski Michael I. Ivanitskiy Nathan Lenssen Douglas Nychka Daniel McKenzie DiffM 16 0 0 14 May 2025
Fast Likelihood-Free Parameter Estimation for Lévy Processes Nicolas Coloma William Kleiber 17 0 0 03 May 2025
Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods Julia Walchessen Amanda Lenzi Mikael Kuusela 35 9 0 31 Dec 2024
When the whole is greater than the sum of its parts: Scaling black-box inference to large data settings through divide-and-conquer Emily C. Hector Amanda Lenzi 41 1 0 31 Dec 2024
A Generalized Unified Skew-Normal Process with Neural Bayes Inference Kesen Wang M. Genton SyDa 66 0 0 26 Nov 2024
Dimension-reduced Reconstruction Map Learning for Parameter Estimation in Likelihood-Free Inference Problems Rui Zhang O. Chkrebtii Dongbin Xiu 26 0 0 19 Jul 2024
Latent Variable Sequence Identification for Cognitive Models with Neural Bayes Estimation Ti-Fen Pan Jing-Jing Li Bill Thompson Anne Collins BDL 29 0 0 20 Jun 2024
A variational neural Bayes framework for inference on intractable posterior distributions Elliot Maceda Emily C. Hector Amanda Lenzi Brian J. Reich 21 2 0 16 Apr 2024
$Deep learning the Hurst parameter of linear fractional processes and assessing its reliability$ Deep learning the Hurst parameter of linear fractional processes and assessing its reliability Dániel Boros Bálint Csanády Iván Ivkovic Lóránt Nagy András Lukács Zsolt László Márkus 8 2 0 03 Jan 2024
Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks Matthew Sainsbury-Dale A. Zammit‐Mangion J. Richards Raphael Huser 28 15 0 04 Oct 2023
Neural Bayes estimators for censored inference with peaks-over-threshold models J. Richards Matthew Sainsbury-Dale A. Zammit‐Mangion Raphael Huser 30 8 0 27 Jun 2023
Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions Nadav Cohen Itzik Klein 3DGS 16 34 0 22 Jun 2023
Universal Approximation and the Topological Neural Network M. Kouritzin Daniel Richard 21 0 0 26 May 2023
Fast parameter estimation of Generalized Extreme Value distribution using Neural Networks Sweta Rai Alexis L Hoffman S. Lahiri D. Nychka S. Sain S. Bandyopadhyay 33 9 0 07 May 2023
Towards black-box parameter estimation Amanda Lenzi Haavard Rue 32 4 0 27 Mar 2023
Likelihood-Free Parameter Estimation with Neural Bayes Estimators Matthew Sainsbury-Dale A. Zammit‐Mangion Raphael Huser 24 34 0 27 Aug 2022
Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks J. Richards Raphael Huser 13 13 0 16 Aug 2022
Statistical Deep Learning for Spatial and Spatio-Temporal Data C. Wikle A. Zammit‐Mangion BDL 19 45 0 05 Jun 2022
Spherical Poisson Point Process Intensity Function Modeling and Estimation with Measure Transport T. L. J. Ng A. Zammit‐Mangion 16 3 0 24 Jan 2022
Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference Niccolò Dalmasso Luca Masserano David Y. Zhao Rafael Izbicki Ann B. Lee 16 5 0 08 Jul 2021
Benchmarking Simulation-Based Inference Jan-Matthis Lueckmann Jan Boelts David S. Greenberg P. J. Gonçalves Jakob H. Macke 101 184 0 12 Jan 2021