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Ensemble Kalman Sampler (EKS) is a method to find approximately $i.i.d.$ samples from a target distribution.
The vlasov dynamics and its fluctuations in the 1 / n 1/n limit of interacting classical particles
W. Braun and K. Hepp · 1977
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Stochastic Stability of Differential Equation
R. Khasminskii · 1980
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Atmospheric sampling
P. Fabian · 1981
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Correlation functions and computer simulations
G. Parisi · 1981
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Best constants in moment inequalities for linear combinations of independent and exchangeable random variables
W. B. Johnson · 1985
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An analysis of particle methods
P. A. Raviart · 1985
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Bayesian inference in econometric models using Monte Carlo integration
J. Geweke · 1989
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Convergence of the point vortex method for the 2-d euler equations
J. Goodman, T. Y. Hou, and J. S. Lowengrub · 1990
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Topics in propagation of chaos
A. Sznitman · 1991
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Asymptotic behaviour of some interacting particle systems; McKean-Vlasov and Boltzmann models
S. Meleard · 1996
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Exponential convergence of Langevin distributions and their discrete approximations
G. Roberts and R. Tweedie · 1996
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Particle methods for dispersive equations
A. Chertock and D. Levy · 2001
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An Introduction to Sequential Monte Carlo Methods
A. Doucet, N. de Freitas, and N. Gordon · 2001
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Sequential Monte Carlo methods in practice
A. Doucet, N. de Freitas, and N. Gordon · 2001
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Langevin diffusions and Metropolis-Hastings algorithms
G. Roberts and O. Stramer · 2002
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Kinetic equilibration rates for granular media and related equations: Entropy dissipation and mass transportation estimates
J. Carrillo, R. McCann, and C. Villani · 2003
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The ensemble Kalman filter: theoretical formulation and practical implementation
G. Evensen · 2003
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General state space Markov chains and MCMC algorithms
G. Roberts and J. Rosenthal · 2004
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Reservoir-fluid sampling and characterization — key to efficient reservoir management
N. Nagarajan, M. Honarpour, and K. Sampath · 2007
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Functions of Matrices: Theory and Computation
N. J. Higham · 2008
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Data Assimilation-The Ensemble Kalman Filter
G. Evensen · 2009
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A localization technique for ensemble Kalman filters
K. Bergemann and S. Reich · 2010
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A mollified ensemble Kalman filter
K. Bergemann and S. Reich · 2010
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Non-asymptotic mixing of the MALA algorithm
N. Bou-Rabee, M. Hairer, and E. Vanden-Eijnden · 2010
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Inverse problems: A Bayesian perspective
A. M. Stuart · 2010
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Recent progress and open problems in algorithmic convex geometry
S. Vempala · 2010
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Stochastic mean-field limit: Non-Llipschitz forces and swarming
F. Bolley, J. A. Cañizo, and J. A. Carrillo · 2011
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Theoretical guarantees for approximate sampling from smooth and log-concave densities
A. S. Dalalyan · 2017
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The Bayesian Approach to Inverse Problems
M. Dashti and A. M. Stuart · 2017
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Rapid mixing of Hamiltonian Monte Carlo on strongly log-concave distributions
O. Mangoubi and A. Smith · 2017
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Analysis of the ensemble Kalman filter for inverse problems
C. Schillings and A. M. Stuart · 2017
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Underdamped Langevin MCMC: A non-asymptotic analysis
X. Cheng, N. Chatterji, P. Bartlett, and M. Jordan · 2018
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Long-time stability and accuracy of the ensemble Kalman–Bucy filter for fully observed processes and small measurement noise
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J. A. Cañizo, J. A. Carrillo, and J. Rosado · 2011
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A dynamical systems framework for intermittent data assimilation
S. Reich · 2011
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MCMC using Hamiltonian dynamics
R. Neal · 2012
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Ensemble Kalman methods for inverse problems
M. Iglesias, K. Law, and A. M. Stuart · 2013
Cited alongside, same era.
Clarification and complement to “mean-field description and propagation of chaos in networks of hodgkin–huxley and fitzhugh–nagumo neurons”
M. Bossy, O. Faugeras, and D. Talay · 2014
Cited alongside, same era.
Stochastic gradient Hamiltonian Monte Carlo
T. Chen, E. B. Fox, and C. Guestrin · 2014
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J. de Wiljes, S. Reich, and W. Stannat · 2018
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Log-concave sampling: Metropolis-Hastings algorithms are fast!
R. Dwivedi, Y. Chen, M. Wainwright, and B. Yu · 2018
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On the mean-field limit for the Vlasov-Poisson-Fokker-Planck system
H. Huang, J. Liu, and P. Pickl · 2018
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Dimensionally tight bounds for second-order Hamiltonian Monte Carlo
O. Mangoubi and N. K. Vishnoi · 2018
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A tutorial on Thompson sampling
D. J. Russo, B. Van Roy, A. Kazerouni, I. Osband, and Z. Wen · 2018
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Convergence analysis of ensemble Kalman inversion: the linear, noisy case
C. Schillings and A. M. Stuart · 2018
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Well posedness and convergence analysis of the ensemble Kalman inversion
D. Bloemker, C. Schillings, P. Wacker, and S. Weissmann · 2019
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Wasserstein stability estimates for covariance-preconditioned fokker-planck equations
J. A. Carrillo and U. Vaes · 2019
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User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient
A. S. Dalalyan and A. Karagulyan · 2019
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Ensemble Kalman inversion: mean-field limit and convergence analysis
Z. Ding and Q. Li · 2019
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Couplings and quantitative contraction rates for Langevin dynamics
A. Eberle, A. Guillin, and R. Zimmer · 2019
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Scaling limit of the stein variational gradient descent: the mean-field regime
J. Lu, Y. Lu, and J. Nolen · 2019
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Accelerating langevin sampling with birth-death
Y. Lu, J. Lu, and J. Nolen · 2019
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Note on interacting Langevin diffusions: Gradient structure and ensemble Kalman sampler by Garbuno-Inigo, Hoffmann, Li and Stuart
N. Nusken and S. Reich · 2019
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Interacting Langevin diffusions: Gradient structure and ensemble Kalman sampler
A. Garbuno-Inigo, F. Hoffmann, W. Li, and A. M. Stuart · 2020
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Random batch methods (rbm) for interacting particle systems
S. Jin, L. Li, and J. Liu · 2020
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