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Optimal experimental design (OED) provides a systematic approach to quantify and maximize the value of experimental data.
On a measure of the information provided by an experiment
D. V. Lindley · 1956
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Theory of Optimal Experiments
V. V. Fedorov · 1972
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On Bayesian methods for seeking the extremum
J. Močkus · 1975
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Expected information as expected utility
J. M. Bernardo · 1979
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A stable and accurate convective modelling procedure based on quadratic upstream interpolation
B. Leonard · 1979
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Comparison of data-driven bandwidth selectors
B. U. Park and J. S. Marron · 1990
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A comparative study of several smoothing methods in density estimation
R. Cao, A. Cuevas, and W. González Manteiga · 1994
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Bayesian experimental design: A review
K. Chaloner and I. Verdinelli · 1995
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Markov chain Monte Carlo convergence diagnostics: A comparative review
M. K. Cowles and B. P. Carlin · 1996
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A brief survey of bandwidth selection for density estimation
M. C. Jones, J. S. Marron, and S. J. Sheather · 1996
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Efficient global optimization of expensive black-box functions
D. R. Jones, M. Schonlau, and W. J. Welch · 1998
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BOA: The Bayesian optimization algorithm
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An Introduction to MCMC for machine learning
C. Andrieu, N. de Freitas, A. Doucet, and M. I. Jordan · 2003
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The IM algorithm: A variational approach to information maximization
D. Barber and F. Agakov · 2003
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Estimating expected information gains for experimental designs with application to the random fatigue-limit model
K. J. Ryan · 2003
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Monte Carlo Statistical Methods
C. P. Robert and G. Casella · 2004
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Parallel tempering: Theory, applications, and new perspectives
D. J. Earl and M. W. Deem · 2005
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Numerical Optimization
J. Nocedal and S. J. Wright · 2006
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Gaussian Processes for Machine Learning
C. E. Rasmussen and C. K. I. Williams · 2006
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Optimum Experimental Designs, With SAS
A. C. Atkinson, A. N. Donev, and R. D. Tobias · 2007
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Ensemble samplers with affine invariance
J. Goodman and J. Weare · 2010
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Handbook of Markov Chain Monte Carlo
S. Brooks, A. Gelman, G. Jones, and X.-L. Meng, editors · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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emcee: The MCMC hammer
D. Foreman-Mackey, D. W. Hogg, D. Lang, and J. Goodman · 2013
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Simulation-based optimal Bayesian experimental design for nonlinear systems
X. Huan and Y. M. Marzouk · 2013
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Fast estimation of expected information gains for Bayesian experimental designs based on Laplace approximations
Q. Long, M. Scavino, R. Tempone, and S. Wang · 2013
Handbook of Approximate Bayesian Computation
S. A. Sisson, Y. Fan, and M. Beaumont · 2018
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A layered multiple importance sampling scheme for focused optimal Bayesian experimental design
C. Feng and Y. M. Marzouk · 2019
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Variational Bayesian optimal experimental design
A. Foster, M. Jankowiak, E. Bingham, P. Horsfall, Y. W. Teh, T. Rainforth, and N. Goodman · 2019
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Error bounds for sequential Monte Carlo samplers for multimodal distributions
D. Paulin, A. Jasra, and A. Thiery · 2019
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On variational bounds of mutual information
B. Poole, S. Ozair, A. Van Den Oord, A. Alemi, and G. Tucker · 2019
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Optimal experimental design for prediction based on push-forward probability measures
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Gradient-based stochastic optimization methods in Bayesian experimental design
X. Huan and Y. M. Marzouk · 2014
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Bayesian Optimization: Open source constrained global optimization tool for Python, 2014
F. Nogueira · 2014
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Optimal Bayesian experimental design for models with intractable likelihoods using indirect inference applied to biological process models
C. M. Ryan, C. C. Drovandi, and A. N. Pettitt · 2016
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A review of modern computational algorithms for Bayesian optimal design
E. G. Ryan, C. C. Drovandi, J. M. Mcgree, and A. N. Pettitt · 2016
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Taking the human out of the loop: A review of Bayesian optimization
B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas · 2016
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Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems
A. Attia, A. Alexanderian, and A. K. Saibaba · 2018
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T. Butler, J. D. Jakeman, and T. Wildey · 2020
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An Introduction to Sequential Monte Carlo Methods
N. Chopin and O. Papaspiliopoulos · 2020
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Surrogates
R. B. Gramacy · 2020
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Bayesian experimental design for implicit models by mutual information neural estimation
S. Kleinegesse and M. U. Gutmann · 2020
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Convergence diagnostics for Markov chain Monte Carlo
V. Roy · 2020
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Expected improvement for expensive optimization: A review
D. Zhan and H. Xing · 2020
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Optimal experimental design for infinite-dimensional Bayesian inverse problems governed by PDEs: A review
A. Alexanderian · 2021
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Generalized parallel tempering on Bayesian inverse problems
J. Latz, J. P. Madrigal-Cianci, F. Nobile, and R. Tempone · 2021
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K. Wu, P. Chen, and O. Ghattas · 2021
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Approximate Laplace importance sampling for the estimation of expected Shannon information gain in high-dimensional Bayesian design for nonlinear models
Y. Englezou, T. W. Waite, and D. C. Woods · 2022
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Stability estimates for the expected utility in Bayesian optimal experimental design
D.-L. Duong, T. Helin, and J. R. Rojo-Garcia · 2023
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Recent advances in Bayesian optimization
X. Wang, Y. Jin, S. Schmitt, and M. Olhofer · 2023
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Optimal experimental design: Formulations and computations
X. Huan, J. Jagalur, and Y. Marzouk · 2024
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Modern Bayesian experimental design
T. Rainforth, A. Foster, D. R. Ivanova, and F. B. Smith · 2024
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