Fetching the paper…
Reading the bibliography…
We present a flexible method for computing Bayesian optimal experimental designs (BOEDs) for inverse problems with intractable posteriors.
“HINT: hierarchical invertible neural transport for density estimation and Bayesian inference”, 2019
Jakob Kruse, Gianluca Detommaso, Ullrich K\"othe and Robert Scheichl · 1905
Earlier work this paper cites.
“Deep composition of tensor-trains using squared inverse Rosenblatt transports”
Tiangang Cui and Sergey Dolgov · 1922
Earlier work this paper cites.
“Remarks on a multivariate transformation”
Murray Rosenblatt · 1952
Earlier work this paper cites.
“On a measure of the information provided by an experiment”
Deenis. Lindley · 1956
Earlier work this paper cites.
“Monte Carlo sampling methods using Markov chains and their applications”
W.. Hastings · 1970
Earlier work this paper cites.
“Foundations of Optimum Experimental Design” Translated from the Czech 14
Andrej P\’azman · 1986
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.
“On choosing and bounding probability metrics”
Alison. Gibbs and Francis Su · 2002
Earlier work this paper cites.
“Optimal Measurement Methods for Distributed Parameter System Identification”, Systems and Control Series
Dariusz Uci\’nski · 2005
Earlier work this paper cites.
“Conditional sampling with monotone GANs: from generative models to likelihood-free inference”, 2020
Ricardo Baptista, Bamdad Hosseini, Nikola. Kovachki and Youssef Marzouk · 2006
Earlier work this paper cites.
“Optimal Design of Experiments” Reprint of the 1993 original 50
F. Pukelsheim · 2006
Earlier work this paper cites.
“Optimum Experimental Designs, with SAS” 34
A.. Atkinson, A.. Donev and R.. Tobias · 2007
Earlier work this paper cites.
“Numerical methods for experimental design of large-scale linear ill-posed inverse problems”
E. Haber, L. Horesh and L. Tenorio · 2008
Earlier work this paper cites.
“On the representation and learning of monotone triangular transport maps”, 2020
Ricardo Baptista, Youssef Marzouk and Olivier Zahm · 2009
Earlier work this paper cites.
“TT-cross approximation for multidimensional arrays”
Ivan Oseledets and Eugene Tyrtyshnikov · 2009
Earlier work this paper cites.
“Optimal Transport” 338
C\’edric Villani · 2009
Earlier work this paper cites.
“Nonlinear model reduction via discrete empirical interpolation”
Saifon Chaturantabut and Danny. Sorensen · 2010
Earlier work this paper cites.
“Simulation-based optimal Bayesian experimental design for nonlinear systems”
Xun Huan and Youssef. Marzouk · 2012
Earlier work this paper cites.
“Bayesian inference with optimal maps”
Tarek. Moselhy and Youssef. Marzouk · 2012
Earlier work this paper cites.
“Fast estimation of expected information gains for Bayesian experimental designs based on Laplace approximations”
Quan Long, Marco Scavino, Ra\’ul Tempone and Suojin Wang · 2013
Earlier work this paper cites.
“A-optimal design of experiments for infinite-dimensional Bayesian linear inverse problems with regularized ℓ 0 \ell_{0} -sparsification”
Alen Alexanderian, Noemi Petra, Georg Stadler and Omar Ghattas · 2014
Cited alongside, same era.
“Alternating minimal energy methods for linear systems in higher dimensions”
Sergey. Dolgov and Dmitry. Savostyanov · 2014
Cited alongside, same era.
“Gradient-based stochastic optimization methods in Bayesian experimental design”
Xun Huan and Youssef Marzouk · 2014
Cited alongside, same era.
“Dimension-independent likelihood-informed MCMC”
Tiangang Cui, Kody.H. Law and Youssef. Marzouk · 2015
Cited alongside, same era.
“A hierarchical multilevel Markov Chain Monte Carlo algorithm with applications to uncertainty quantification in subsurface flow”
T.. Dodwell, C. Ketelsen, R. Scheichl and A.. Teckentrup · 2015
Cited alongside, same era.
“Greedy inference with structure-exploiting lazy maps”
Michael Brennan et al · 2020
Later among the works it cites.
“A unified stochastic gradient approach to designing Bayesian-optimal experiments”
Adam Foster et al · 2020
Later among the works it cites.
“Optimal experimental design under irreducible uncertainty for linear inverse problems governed by PDEs”
Karina Koval, Alen Alexanderian and Georg Stadler · 2020
Later among the works it cites.
“Optimal experimental design for infinite-dimensional Bayesian inverse problems governed by PDEs: a review”
Alen Alexanderian · 2021
Later among the works it cites.
“Optimal design of large-scale Bayesian linear inverse problems under reducible model uncertainty: good to know what you don’t know”
Alen Alexanderian, Noemi Petra, Georg Stadler and Isaac Sunseri · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“Numerical Approaches for Sequential Bayesian Optimal Experimental Design”
Xun Huan · 2015
Cited alongside, same era.
“On Bayesian A- and D-optimal experimental designs in infinite dimensions”
Alen Alexanderian, Philip. Gloor and Omar Ghattas · 2016
Cited alongside, same era.
“A fast and scalable method for A-optimal design of experiments for infinite-dimensional Bayesian nonlinear inverse problems”
Alen Alexanderian, Noemi Petra, Georg Stadler and Omar Ghattas · 2016
Cited alongside, same era.
“Spectral tensor-train decomposition”
Daniele Bigoni, Allan. Engsig-Karup and Youssef. Marzouk · 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.
“Sampling via measure transport: an introduction”
Youssef Marzouk, Tarek Moselhy, Matthew Parno and Alessio Spantini · 2016
Cited alongside, same era.
“Efficient D-optimal design of experiments for infinite-dimensional Bayesian linear inverse problems”
Alen Alexanderian and Arvind. Saibaba · 2018
Cited alongside, same era.
“Deep adaptive design: amortizing sequential Bayesian experimental design”
Adam Foster, Desi Ivanova, Ilyas Malik and Tom Rainforth · 2021
Later among the works it cites.
“Analysis of tensor approximation schemes for continuous functions”
Michael Griebel and Helmut Harbrecht · 2021
Later among the works it cites.
“Normalizing flows for probabilistic modeling and inference”
George Papamakarios et al · 2021
Later among the works it cites.
“Optimal design of large-scale nonlinear Bayesian inverse problems under model uncertainty”, 2022
Alen Alexanderian, Ruanui Nicholson and Noemi Petra · 2022
Later among the works it cites.
“Optimal experimental design for inverse problems in the presence of observation correlations”
Ahmed Attia and Emil Constantinescu · 2022
Later among the works it cites.
“Stochastic learning approach for binary optimization: application to Bayesian optimal design of experiments”
Ahmed Attia, Sven Leyffer and Todd. Munson · 2022
Later among the works it cites.
Ricardo Baptista et al · 2022
Later among the works it cites.
“Fast Forward and Inverse problems solver (FastFInS)”, 2022
Tiangang Cui · 2022
Later among the works it cites.
“Rank bounds for approximating Gaussian densities in the tensor-train format”
Paul. Rohrbach, Sergey Dolgov, Lars Grasedyck and Robert Scheichl · 2022
Later among the works it cites.
Keyi Wu, Thomas O’Leary-Roseberry, Peng Chen and Omar Ghattas · 2022
Later among the works it cites.
“Deep Inverse Rosenblatt Transport (DIRT)”, 2023
Tiangang Cui · 2023
Later among the works it cites.
“Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction”
Tiangang Cui, Sergey Dolgov and Olivier Zahm · 2023
Later among the works it cites.
“A fast and scalable computational framework for large-scale high-dimensional Bayesian optimal experimental design”
Keyi Wu, Peng Chen and Omar Ghattas · 2023
Later among the works it cites.
“Tensor-train methods for sequential state and parameter learning in state-space models”, 2023
Yiran Zhao and Tiangang Cui · 2023
Later among the works it cites.