Truong‐Vinh Hoang

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Common Coauthors
Commonly Cited References
Action Title Year Authors # of times referenced
+ Functional Analysis for Probability and Stochastic Processes 2005 Adam Bobrowski
1
+ Bayesian Inverse Problems and Kalman Filters 2014 Oliver G. Ernst
Björn Sprungk
Hans-Jörg Starkloff
1
+ Data Assimilation: A Mathematical Introduction 2015 Kody J. H. Law
Andrew M. Stuart
Konstantinos C. Zygalakis
1
+ PDF Chat Multilevel ensemble Kalman filtering 2016 Håkon Hoel
Kody J. H. Law
Raúl Tempone
1
+ Estimation and Inference via Bayesian Simulation: An Introduction to Markov Chain Monte Carlo 2000 Simon Jackman
1
+ PDF Chat Imprecise probabilities in engineering analyses 2013 Michael Beer
Scott Ferson
Владик Крейнович
1
+ Riemannian Manifold Learning 2008 Tong Lin
Hongbin Zha
1
+ PDF Chat Markov Chains for Exploring Posterior Distributions 1994 Luke Tierney
1
+ PDF Chat Deterministic Nonperiodic Flow 1963 Edward N. Lorenz
1
+ Deterministic Mean-Field Ensemble Kalman Filtering 2016 Kody J. H. Law
Hamidou Tembiné
Raúl Tempone
1
+ Probability: Theory and Examples. 1992 Kathryn Prewitt
Richard Durrett
1
+ PDF Chat Uncertainty propagation of p-boxes using sparse polynomial chaos expansions 2017 Roland Schöbi
Bruno Sudret
1
+ PDF Chat A-optimal encoding weights for nonlinear inverse problems, with application to the Helmholtz inverse problem 2017 Benjamin Crestel
Alen Alexanderian
Georg Stadler
Omar Ghattas
1
+ PDF Chat Accurate Computation of Conditional Expectation for Highly Nonlinear Problems 2019 Jaroslav Vondřejc
Hermann G. Matthies
1
+ PDF Chat Coupling Techniques for Nonlinear Ensemble Filtering 2022 Alessio Spantini
Ricardo Baptista
Youssef Marzouk
1
+ PDF Chat Parameter estimation via conditional expectation: a Bayesian inversion 2016 Hermann G. Matthies
Elmar Zander
Bojana Rosić
Alexander Litvinenko
1
+ PDF Chat Inverse Problems in a Bayesian Setting 2016 Hermann G. Matthies
Elmar Zander
Bojana Rosić
Alexander Litvinenko
Oliver Pajonk
1
+ Adam: A Method for Stochastic Optimization 2014 Diederik P. Kingma
Jimmy Ba
1
+ PDF Chat Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model 2020 David John Gagne
Hannah M. Christensen
Aneesh Subramanian
Adam H. Monahan
1
+ PDF Chat Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets 2021 Christian Soize
Roger Ghanem
1
+ Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization 2020 Marc Bocquet
Julien Brajard
Alberto Carrassi
Laurent Bertino
1
+ Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model 2020 Julien Brajard
Alberto Carrassi
Marc Bocquet
Laurent Bertino
1
+ PDF Chat McKean--Vlasov SDEs in Nonlinear Filtering 2021 Sahani Pathiraja
Sebastian Reich
Wilhelm Stannat
1
+ Coupling techniques for nonlinear ensemble filtering 2019 Alessio Spantini
R. Baptista
Youssef Marzouk
1