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On posterior consistency of data assimilation with Gaussian process priors: the 2D Navier-Stokes equations
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On posterior consistency of data assimilation with Gaussian process priors: the 2D Navier-Stokes equations

Richard Nickl
E. Titi
ArXiv (abs)PDFHTML

Papers citing "On posterior consistency of data assimilation with Gaussian process priors: the 2D Navier-Stokes equations"

6 / 6 papers shown
Title
Forecasting time series with constraints
Forecasting time series with constraints
Nathan Doumèche
Francis Bach
Éloi Bedek
Gérard Biau
Claire Boyer
Y. Goude
AI4TS
203
0
0
14 Feb 2025
Convergence Rates for the Maximum A Posteriori Estimator in PDE-Regression Models with Random Design
Convergence Rates for the Maximum A Posteriori Estimator in PDE-Regression Models with Random Design
Maximilian Siebel
51
1
0
05 Sep 2024
Bayesian Nonparametric Inference in McKean-Vlasov models
Bayesian Nonparametric Inference in McKean-Vlasov models
Richard Nickl
G. Pavliotis
Kolyan Ray
42
5
0
25 Apr 2024
Distribution learning via neural differential equations: a nonparametric statistical perspective
Distribution learning via neural differential equations: a nonparametric statistical perspective
Youssef Marzouk
Zhi Ren
Sven Wang
Jakob Zech
87
12
0
03 Sep 2023
Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators Under Mesh Refinement
Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators Under Mesh Refinement
D. Sanz-Alonso
Nathan Waniorek
83
5
0
19 Apr 2023
Consistent inference for diffusions from low frequency measurements
Consistent inference for diffusions from low frequency measurements
Richard Nickl
59
8
0
24 Oct 2022