Ensemble Kalman methods for high-dimensional hierarchical dynamic space-time models

23 April 2017

Papers citing "Ensemble Kalman methods for high-dimensional hierarchical dynamic space-time models"

8 / 8 papers shown

Title
Uncertainty quantification for deeponets with ensemble kalman inversion Andrew Pensoneault Xueyu Zhu 26 1 0 06 Mar 2024
Scalable Spatio-Temporal Smoothing via Hierarchical Sparse Cholesky Decomposition M. Jurek Matthias Katzfuss 19 9 0 19 Jul 2022
Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality Ning Ning E. Ionides 16 13 0 20 Oct 2021
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks Shiwei Lan Shuyi Li Babak Shahbaba UQCV BDL 25 16 0 11 Jan 2021
Deep Integro-Difference Equation Models for Spatio-Temporal Forecasting A. Zammit‐Mangion C. Wikle 8 47 0 29 Oct 2019
Multi-resolution filters for massive spatio-temporal data M. Jurek Matthias Katzfuss 14 24 0 09 Oct 2018
Gaussian variational approximation for high-dimensional state space models M. Quiroz David J. Nott Robert Kohn 24 40 0 24 Jan 2018
A Bayesian adaptive ensemble Kalman filter for sequential state and parameter estimation Jonathan R. Stroud Matthias Katzfuss C. Wikle 33 55 0 11 Nov 2016