Fetching the paper…
Reading the bibliography…
In this paper, we study efficient approximate sampling for probability distributions known up to normalization constants.
Nonlinear Bayesian estimation using Gaussian sum approximations
Daniel Alspach and Harold Sorenson · 1972
Earlier work this paper cites.
Numerical analysis of blood flow in the heart
Charles S Peskin · 1977
Earlier work this paper cites.
Local adaptive mesh refinement for shock hydrodynamics
Marsha J Berger, Phillip Colella, et al · 1989
Earlier work this paper cites.
Information and the accuracy attainable in the estimation of statistical parameters
C Radhakrishna Rao · 1992
Earlier work this paper cites.
Natural gradient works efficiently in learning
Shun-Ichi Amari · 1998
Earlier work this paper cites.
Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
Andrew Gelman and Xiao-Li Meng · 1998
Earlier work this paper cites.
A finite element method for crack growth without remeshing
Nicolas Moës, John Dolbow, and Ted Belytschko · 1999
Earlier work this paper cites.
An introduction to variational methods for graphical models
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
Earlier work this paper cites.
Statistical decision rules and optimal inference
Nikolai Nikolaevich Cencov · 2000
Earlier work this paper cites.
Gaussian filters for nonlinear filtering problems
Kazufumi Ito and Kaiqi Xiong · 2000
Earlier work this paper cites.
Mixture kalman filters
Rong Chen and Jun S Liu · 2000
Earlier work this paper cites.
Annealed importance sampling
Radford M Neal · 2001
Earlier work this paper cites.
Gaussian mixture sigma-point particle filters for sequential probabilistic inference in dynamic state-space models
Rudolph Van Der Merwe and Eric Wan · 2003
Earlier work this paper cites.
Toward a nonlinear ensemble filter for high-dimensional systems
Thomas Bengtsson, Chris Snyder, and Doug Nychka · 2003
Earlier work this paper cites.
Metastability in reversible diffusion processes i: Sharp asymptotics for capacities and exit times
Véronique Gayrard, Anton Bovier, Michael Eckhoff, and Markus Klein · 2004
Earlier work this paper cites.
Quantifying uncertainty in projections of regional climate change: A Bayesian approach to the analysis of multimodel ensembles
Claudia Tebaldi, Richard L Smith, Doug Nychka, and Linda O Mearns · 2005
Earlier work this paper cites.
Metastability in reversible diffusion processes ii: Precise asymptotics for small eigenvalues
Véronique Gayrard, Anton Bovier, and Markus Klein · 2005
Earlier work this paper cites.
Statistical and computational inverse problems
Jari Kaipio and Erkki Somersalo · 2006
Earlier work this paper cites.
Sequential Monte Carlo samplers
Pierre Del Moral, Arnaud Doucet, and Ajay Jasra · 2006
Earlier work this paper cites.
Cluster ensemble kalman filter
Keston W Smith · 2007
Earlier work this paper cites.
Graphical models, exponential families, and variational inference
Martin J Wainwright, Michael I Jordan, et al · 2008
Earlier work this paper cites.
A tutorial on particle filtering and smoothing: Fifteen years later
Arnaud Doucet, Adam M Johansen, et al · 2009
Earlier work this paper cites.
Sequential updating of multimodal hydrogeologic parameter fields using localization and clustering techniques
Alexander Y Sun, Alan P Morris, and Sitakanta Mohanty · 2009
Earlier work this paper cites.
Inverse problems: A Bayesian perspective
Andrew M Stuart · 2010
Earlier work this paper cites.
Ensemble samplers with affine invariance
Jonathan Goodman and Jonathan Weare · 2010
Earlier work this paper cites.
Handbook of Markov chain Monte Carlo
Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng · 2011
Earlier work this paper cites.
Bridging the ensemble kalman filter and particle filters: the adaptive Gaussian mixture filter
Andreas S Stordal, Hans A Karlsen, Geir Nævdal, Hans J Skaug, and Brice Vallès · 2011
Earlier work this paper cites.
A dynamical systems framework for intermittent data assimilation
Sebastian Reich · 2011
Earlier work this paper cites.
Ensemble randomized maximum likelihood method as an iterative ensemble smoother
Yan Chen and Dean S Oliver · 2012
Earlier work this paper cites.
A Gaussian-mixture ensemble transform filter
Sebastian Reich · 2012
Earlier work this paper cites.
Particle Kalman filtering: A nonlinear Bayesian framework for ensemble kalman filters
Ibrahim Hoteit, Xiaodong Luo, and Dinh-Tuan Pham · 2012
Earlier work this paper cites.
Filtering with state space localized kalman gain
Andreas S Stordal, Hans A Karlsen, Geir Nævdal, Dean S Oliver, and Hans J Skaug · 2012
Cited alongside, same era.
Global optimization methods in geophysical inversion
Mrinal K Sen and Paul L Stoffa · 2013
Cited alongside, same era.
Investigation of the sampling performance of ensemble-based methods with a simple reservoir model
Alexandre A Emerick and Albert C Reynolds · 2013
Cited alongside, same era.
Ensemble Kalman methods for inverse problems
Marco A Iglesias, Kody JH Law, and Andrew M Stuart · 2013
Cited alongside, same era.
Mixture ensemble kalman filters
Marco Frei and Hans R Künsch · 2013
Cited alongside, same era.
EMCEE: The MCMC hammer
Daniel Foreman-Mackey, David W Hogg, Dustin Lang, and Jonathan Goodman · 2013
Cited alongside, same era.
Computing quantum dynamics in the semiclassical regime
Caroline Lasser and Christian Lubich · 2020
Later among the works it cites.
Interacting Langevin diffusions: Gradient structure and ensemble Kalman sampler
Alfredo Garbuno-Inigo, Franca Hoffmann, Wuchen Li, and Andrew M Stuart · 2020
Later among the works it cites.
Affine invariant interacting Langevin dynamics for Bayesian inference
Alfredo Garbuno-Inigo, Nikolas Nüsken, and Sebastian Reich · 2020
Later among the works it cites.
Flexible and efficient inference with particles for the variational Gaussian approximation
Théo Galy-Fajou, Valerio Perrone, and Manfred Opper · 2021
Later among the works it cites.
Fokker–Planck particle systems for Bayesian inference: Computational approaches
Sebastian Reich and Simon Weissmann · 2021
Later among the works it cites.
Bayesian calibration for large-scale fluid structure interaction problems under embedded/immersed boundary framework
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Information geometry and sufficient statistics
Nihat Ay, Jürgen Jost, Hông Vân Lê, and Lorenz Schwachhöfer · 2015
Cited alongside, same era.
Analysis of the ensemble and polynomial chaos Kalman filters in Bayesian inverse problems
Oliver G Ernst, Björn Sprungk, and Hans-Jörg Starkloff · 2015
Cited alongside, same era.
Uniqueness of the Fisher–Rao metric on the space of smooth densities
Martin Bauer, Martins Bruveris, and Peter W Michor · 2016
Cited alongside, same era.
Gaussian mixture model-based ensemble kalman filtering for state and parameter estimation for a pmma process
Ruoxia Li, Vinay Prasad, and Biao Huang · 2016
Cited alongside, same era.
Stein variational gradient descent: A general purpose Bayesian inference algorithm
Qiang Liu and Dilin Wang · 2016
Cited alongside, same era.
Earth system modeling 2.0: A blueprint for models that learn from observations and targeted high-resolution simulations
Tapio Schneider, Shiwei Lan, Andrew Stuart, and Joao Teixeira · 2017
Cited alongside, same era.
Shunxiang Cao and Daniel Zhengyu Huang · 2022
Later among the works it cites.
Training physics-based machine-learning parameterizations with gradient-free ensemble kalman methods
Ignacio Lopez-Gomez, Costa D Christopoulos, Haakon Ludvig Ervik, Oliver R Dunbar, Yair Cohen, and Tapio Schneider · 2022
Later among the works it cites.
Birth-death dynamics for sampling: Global convergence, approximations and their asymptotics
Yulong Lu, Dejan Slepčev, and Lihan Wang · 2022
Later among the works it cites.
Variational inference via wasserstein gradient flows
Marc Lambert, Sinho Chewi, Francis Bach, Silvère Bonnabel, and Philippe Rigollet · 2022
Later among the works it cites.
Iterated kalman methodology for inverse problems
Daniel Zhengyu Huang, Tapio Schneider, and Andrew M Stuart · 2022
Later among the works it cites.
Ensemble Kalman methods: a mean field perspective
Edoardo Calvello, Sebastian Reich, and Andrew M Stuart · 2022
Later among the works it cites.
Efficient derivative-free Bayesian inference for large-scale inverse problems
Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, and Andrew M Stuart · 2022
Later among the works it cites.
A residual-driven adaptive Gaussian mixture approximation for Bayesian inverse problems
Yuming Ba and Lijian Jiang · 2022
Later among the works it cites.
Derivative-free Bayesian inversion using multiscale dynamics
Grigoris A Pavliotis, Andrew M Stuart, and Urbain Vaes · 2022
Later among the works it cites.
From optimization to sampling through gradient flows
N Garcia Trillos, B Hosseini, and D Sanz-Alonso · 2023
Later among the works it cites.
Sampling via gradient flows in the space of probability measures
Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, and Andrew M Stuart · 2023
Later among the works it cites.
An explicit expansion of the kullback-leibler divergence along its fisher-rao gradient flow
Carles Domingo-Enrich and Aram-Alexandre Pooladian · 2023
Later among the works it cites.
Accelerate langevin sampling with birth-death process and exploration component
Lezhi Tan and Jianfeng Lu · 2023
Later among the works it cites.
Gradient flows for sampling: Mean-field models, Gaussian approximations and affine invariance
Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, and Andrew M Stuart · 2023
Later among the works it cites.
Ilja Klebanov and Timothy John Sullivan · 2023
Later among the works it cites.
Learning Gaussian mixtures using the wasserstein-fisher-rao gradient flow
Yuling Yan, Kaizheng Wang, and Philippe Rigollet · 2023
Later among the works it cites.
A connection between tempering and entropic mirror descent
Nicolas Chopin, Francesca R Crucinio, and Anna Korba · 2023
Later among the works it cites.
Theoretical guarantees for variational inference with fixed-variance mixture of gaussians
Tom Huix, Anna Korba, Alain Durmus, and Eric Moulines · 2024
Closest in time.
Sampling in unit time with kernel fisher-rao flow
Aimee Maurais and Youssef Marzouk · 2024
Closest in time.
Stein transport for Bayesian inference
Nikolas Nüsken · 2024
Closest in time.
Fisher information and shape-morphing modes for solving the fokker–planck equation in higher dimensions
William Anderson and Mohammad Farazmand · 2024
Closest in time.
Huan Zhang, Yifan Chen, Eric Vanden-Eijnden, and Benjamin Peherstorfer · 2024
Closest in time.
Measure transport with kernel mean embeddings
L Wang and N Nüsken · 2024
Closest in time.
Fisher-rao gradient flow: Geodesic convexity and functional inequalities
José A Carrillo, Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, and Dongyi Wei · 2024
Closest in time.
Ensemble-based annealed importance sampling
Haoxuan Chen and Lexing Ying · 2024
Closest in time.