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Data assimilation is concerned with sequentially estimating a temporally-evolving state.
A new approach to linear filtering and prediction problems
Rudolph Emil Kalman · 1960
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Propagation of chaos for a class of non-linear parabolic equations
Henry P McKean · 1967
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Maximum likelihood from incomplete data via the EM algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
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A Monte Carlo implementation of the EM algorithm and the poor man’s data augmentation algorithms
Greg CG Wei and Martin A Tanner · 1990
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Topics in propagation of chaos
Alain-Sol Sznitman · 1991
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Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Neil J Gordon, David J Salmond, and Adrian FM Smith · 1993
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Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics
Geir Evensen · 1994
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Gradient-based learning algorithms for recurrent
Ronald J Williams and David Zipser · 1995
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Predictability: A problem partly solved
Edward N Lorenz · 1996
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Recursive identification in hidden markov models
François Le Gland and Laurent Mevel · 1997
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Data assimilation using an ensemble Kalman filter technique
Peter L Houtekamer and Herschel L Mitchell · 1998
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A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts
Jeffrey L Anderson and Stephen L Anderson · 1999
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Construction of correlation functions in two and three dimensions
Gregory Gaspari and Stephen E Cohn · 1999
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On sequential Monte Carlo sampling methods for Bayesian filtering
Arnaud Doucet, Simon Godsill, and Christophe Andrieu · 2000
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An ensemble adjustment Kalman filter for data assimilation
Jeffrey L Anderson · 2001
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Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter
Thomas M Hamill, Jeffrey S Whitaker, and Chris Snyder · 2001
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A sequential ensemble Kalman filter for atmospheric data assimilation
Peter L Houtekamer and Herschel L Mitchell · 2001
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Feynman-Kac Formulae: Genealogical and Interacting Particle Systems with Applications
Pierre Del Moral · 2004
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A local ensemble Kalman filter for atmospheric data assimilation
Edward Ott, Brian R Hunt, Istvan Szunyogh, Aleksey V Zimin, Eric J Kostelich, Matteo Corazza, Eugenia Kalnay, DJ Patil, and James A Yorke · 2004
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Pattern Recognition and Machine Learning
Christopher M Bishop · 2006
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Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants
Reinhard Furrer and Thomas Bengtsson · 2007
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Stochastic processes and filtering theory
Andrew H Jazwinski · 2007
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Sequential state and variance estimation within the ensemble Kalman filter
Jonathan R Stroud and Thomas Bengtsson · 2007
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Estimation of parameterized spatio-temporal dynamic models
Ke Xu and Christopher K Wikle · 2007
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Collected matrix derivative results for forward and reverse mode algorithmic differentiation
Mike B Giles · 2008
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Obstacles to high-dimensional particle filtering
Chris Snyder, Thomas Bengtsson, Peter Bickel, and Jeff Anderson · 2008
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A local ensemble transform Kalman filter data assimilation system for the NCEP global model
Istvan Szunyogh, Eric J Kostelich, Gyorgyi Gyarmati, Eugenia Kalnay, Brian R Hunt, Edward Ott, Elizabeth Satterfield, and James A Yorke · 2008
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Ensemble data assimilation with the ncep global forecast system
Jeffrey S Whitaker, Thomas M Hamill, Xue Wei, Yucheng Song, and Zoltan Toth · 2008
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A tutorial on particle filtering and smoothing: Fifteen years later
Arnaud Doucet and Adam M Johansen · 2009
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Data assimilation: the ensemble Kalman filter
Geir Evensen · 2009
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Large sample asymptotics for the ensemble Kalman filter
François Le Gland, Valérie Monbet, and Vu-Duc Tran · 2009
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Particle Markov chain Monte Carlo methods
Christophe Andrieu, Arnaud Doucet, and Roman Holenstein · 2010
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Beyond Gaussian statistical modeling in geophysical data assimilation
Marc Bocquet, Carlos A Pires, and Lin Wu · 2010
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State and parameter estimation in stochastic dynamical models
Timothy DelSole and Xiaosong Yang · 2010
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An ensemble Kalman filter and smoother for satellite data assimilation
Jonathan R Stroud, Michael L Stein, Barry M Lesht, David J Schwab, and Dmitry Beletsky · 2010
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Relation between two common localisation methods for the EnKF
Pavel Sakov and Laurent Bertino · 2011
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A disentangled recognition and nonlinear dynamics model for unsupervised learning
Marco Fraccaro, Simon Kamronn, Ulrich Paquet, and Ole Winther · 2017
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Structured inference networks for nonlinear state space models
Rahul Krishnan, Uri Shalit, and David Sontag · 2017
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Auto-encoding sequential Monte Carlo
Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, and Frank Wood · 2017
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Filtering variational objectives
Chris J Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, and Yee Whye Teh · 2017
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The Ensemble Kalman filter: a signal processing perspective
Michael Roth, Gustaf Hendeby, Carsten Fritsche, and Fredrik Gustafsson · 2017
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Evaluating data assimilation algorithms
Kody JH Law and Andrew M Stuart · 2012
Cited alongside, same era.
Filtering Complex Turbulent Systems
Andrew J Majda and John Harlim · 2012
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On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Auto-Encoding Variational Bayes
Diederik P. Kingma and Max Welling · 2014
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Optimal filtering and the dual process
Omiros Papaspiliopoulos, Matteo Ruggiero, et al · 2014
Cited alongside, same era.
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Analysis of the ensemble Kalman filter for inverse problems
Claudia Schillings and Andrew M Stuart · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2018
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On the stability and the uniform propagation of chaos properties of ensemble Kalman–Bucy filters
Pierre Del Moral, Julian Tugaut, et al · 2018
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Variational sequential Monte Carlo
Christian Naesseth, Scott Linderman, Rajesh Ranganath, and David Blei · 2018
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Stochastic parameterization identification using ensemble Kalman filtering combined with maximum likelihood methods
Manuel Pulido, Pierre Tandeo, Marc Bocquet, Alberto Carrassi, and Magdalena Lucini · 2018
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Deep state space models for time series forecasting
Syama Sundar Rangapuram, Matthias Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski · 2018
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Importance sampling and necessary sample size: an information theory approach
Daniel Sanz-Alonso · 2018
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A Bayesian adaptive ensemble Kalman filter for sequential state and parameter estimation
Jonathan R Stroud, Matthias Katzfuss, and Christopher K Wikle · 2018
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Learning dynamical systems from partial observations
Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot, Julien Brajard, and Patrick Gallinari · 2019
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Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models
Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino · 2019
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Gru-ode-bayes: Continuous modeling of sporadically-observed time series
Edward De Brouwer, Jaak Simm, Adam Arany, and Yves Moreau · 2019
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Estimating model evidence using ensemble-based data assimilation with localization–The model selection problem
Sammy Metref, Alexis Hannart, Juan Ruiz, Marc Bocquet, Alberto Carrassi, and Michael Ghil · 2019
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Em-like learning chaotic dynamics from noisy and partial observations
Duong Nguyen, Said Ouala, Lucas Drumetz, and Ronan Fablet · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Latent ordinary differential equations for irregularly-sampled time series
Yulia Rubanova, Ricky TQ Chen, and David Duvenaud · 2019
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Inverse Problems and Data Assimilation
Daniel Sanz-Alonso, Andrew M Stuart, and Armeen Taeb · 2019
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Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino · 2020
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Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the lorenz 96 model
Julien Brajard, Alberto Carrassi, Marc Bocquet, and Laurent Bertino · 2020
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Iterative ensemble Kalman methods: a unified perspective with some new variants
Neil K Chada, Yuming Chen, and Daniel Sanz-Alonso · 2020
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Ensemble Kalman methods for high-dimensional hierarchical dynamic space-time models
Matthias Katzfuss, Jonathan R Stroud, and Christopher K Wikle · 2020
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Model error covariance estimation in particle and ensemble Kalman filters using an online expectation–maximization algorithm
Tadeo J Cocucci, Manuel Pulido, Magdalena Lucini, and Pierre Tandeo · 2021
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Differentiable particle filtering via entropy-regularized optimal transport
Adrien Corenflos, James Thornton, Arnaud Doucet, and George Deligiannidis · 2021
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Ensemble MCMC: accelerating pseudo-marginal MCMC for state space models using the ensemble Kalman filter
Christopher Drovandi, Richard G Everitt, Andrew Golightly, Dennis Prangle, et al · 2021
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Bayesian update with importance sampling: Required sample size
Daniel Sanz-Alonso and Zijian Wang · 2021
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