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Many recent works in simulation-based inference (SBI) rely on deep generative models to approximate complex, high-dimensional posterior distributions.
Likelihood-free MCMC with amortized approximate ratio estimators
Joeri Hermans, Volodimir Begy, and Gilles Louppe · 1903
Earlier work this paper cites.
Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 1912
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Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns
Ben H. Jansen and Vincent G. Rit · 1995
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Inferring coalescence times from DNA sequence data
Simon Tavaré, David J. Balding, R. C. Griffiths, and Peter Donnelly · 1997
Earlier work this paper cites.
Monte Carlo statistical methods
Christian Robert and George Casella · 2005
Earlier work this paper cites.
The elements of statistical learning
Trevor Hastie, Robert Tibshirani, and J H Friedman · 2009
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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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Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Michael U. Gutmann and Aapo Hyvärinen · 2012
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The virtual brain: a simulator of primate brain network dynamics
Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide, Jochen Mersmann, Anthony R. Mcintosh, and Viktor Jirsa · 2013
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Institute of mathematical statistics monographs: Computer age statistical inference: Algorithms, evidence, and data science series number 5
Bradley Efron and Trevor Hastie · 2016
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Revisiting classifier two-sample tests
David Lopez-Paz and Maxime Oquab · 2016
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Stan: A probabilistic programming language
Bob Carpenter, Andrew Gelman, Matthew D Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell · 2017
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Masked autoregressive flow for density estimation
George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
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Global and local two-sample tests via regression
Ilmun Kim, Ann B. Lee, and Jing Lei · 2018
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Validating bayesian inference algorithms with simulation-based calibration
Sean Talts, Michael Betancourt, Daniel Simpson, Aki Vehtari, and Andrew Gelman · 2018
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Neural spline flows
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2019
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Model misspecification in approximate Bayesian computation: consequences and diagnostics
David Frazier, Christian Robert, and Judith Rousseau · 2019
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Automatic posterior transformation for likelihood-free inference
David Greenberg, Marcel Nonnenmacher, and Jakob Macke · 2019
Cited alongside, same era.
Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows
George Papamakarios, David Sterratt, and Iain Murray · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Lossless, scalable implicit likelihood inference for cosmological fields
T. Lucas Makinen, Tom Charnock, Justin Alsing, and Benjamin D. Wandelt · 2021
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Diagnostics for conditional density models and bayesian inference algorithms
David Zhao, Niccolò Dalmasso, Rafael Izbicki, and Ann B. Lee · 2021
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Group equivariant neural posterior estimation
Maximilian Dax, Stephen R Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, and Jakob H. Macke · 2022
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Towards reliable simulation-based inference with balanced neural ratio estimation
Arnaud Delaunoy, Joeri Hermans, François Rozet, Antoine Wehenkel, and Gilles Louppe · 2022
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Calibrated predictive distributions via diagnostics for conditional coverage
Biprateep Dey, David Zhao, Jeffrey A. Newman, Brett H. Andrews, Rafael Izbicki, and Ann B. Lee · 2022
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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
Cited alongside, same era.
The frontier of simulation-based inference
Kyle Cranmer, Johann Brehmer, and Gilles Louppe · 2020
Cited alongside, same era.
Confidence sets and hypothesis testing in a likelihood-free inference setting
Niccolo Dalmasso, Rafael Izbicki, and Ann Lee · 2020
Cited alongside, same era.
nflows: normalizing flows in PyTorch
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2020
Cited alongside, same era.
Bayesian workflow, 2020
Andrew Gelman, Aki Vehtari, Daniel Simpson, Charles C. Margossian, Bob Carpenter, Yuling Yao, Lauren Kennedy, Jonah Gabry, Paul-Christian Bürkner, and Martin Modrák · 2020
Cited alongside, same era.
Training deep neural density estimators to identify mechanistic models of neural dynamics
Pedro J. Gonçalves, Jan Matthis Lueckmann, Michael Deistler, Marcel Nonnenmacher, Kaan Öcal, Giacomo Bassetto, Chaitanya Chintaluri, William F. Podlaski, Sara A. Haddad, Tim P. Vogels, David S. Greenberg, and Jakob H. Macke · 2020
Cited alongside, same era.
sbi: A toolkit for simulation-based inference
Alvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro J. Gonçalves, David S. Greenberg, and Jakob H. Macke · 2020
Cited alongside, same era.
Simulation-based inference for whole-brain network modeling of epilepsy using deep neural density estimators
Meysam Hashemi, Anirudh N. Vattikonda, Jayant Jha, Viktor Sip, Marmaduke M. Woodman, Fabrice Bartolomei, and Viktor K. Jirsa · 2022
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A crisis in simulation-based inference? beware, your posterior approximations can be unfaithful
Joeri Hermans, Arnaud Delaunoy, François Rozet, Antoine Wehenkel, Volodimir Begy, and Gilles Louppe · 2022
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Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements
Maëliss Jallais, Pedro L. C. Rodrigues, Alexandre Gramfort, and Demian Wassermann · 2022
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Validation diagnostics for SBI algorithms based on normalizing flows, 2022
Julia Linhart, Alexandre Gramfort, and Pedro L. C. Rodrigues · 2022
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Martin Modrák, Angie H. Moon, Shinyoung Kim, Paul Bürkner, Niko Huurre, Kateřina Faltejsková, Andrew Gelman, and Aki Vehtari · 2022
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E-valuating classifier two-sample tests, 2022
Teodora Pandeva, Tim Bakker, Christian A. Naesseth, and Patrick Forré · 2022
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Robust neural posterior estimation and statistical model criticism
Daniel Ward, Patrick Cannon, Mark Beaumont, Matteo Fasiolo, and Sebastian M Schmon · 2022
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A large-scale study of probabilistic calibration in neural network regression
Victor Dheur and Souhaib Ben Taieb · 2023
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Robust simulation-based inference in cosmology with bayesian neural networks
Pablo Lemos, Miles Cranmer, Muntazir Abidi, ChangHoon Hahn, Michael Eickenberg, Elena Massara, David Yallup, and Shirley Ho · 2023
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Neural posterior estimation for exoplanetary atmospheric retrieval
Vasist, Malavika, Rozet, François, Absil, Olivier, Mollière, Paul, Nasedkin, Evert, and Louppe, Gilles · 2023
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Discriminative calibration
Yuling Yao and Justin Domke · 2023
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