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Modern approaches for simulation-based inference rely upon deep learning surrogates to enable approximate inference with computer simulators.
Analytical note on certain rhythmic relations in organic systems
Alfred J Lotka · 1920
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Fluctuations in the abundance of a species considered mathematically
Vito Volterra · 1926
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Bayesian inference in statistical analysis , volume 40
George EP Box and George C Tiao · 1973
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Inference from iterative simulation using multiple sequences
Andrew Gelman and Donald B Rubin · 1992
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Computing and graphing highest density regions
Rob J Hyndman · 1996
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Density-ratio matching under the bregman divergence: a unified framework of density-ratio estimation
Masashi Sugiyama, Taiji Suzuki, and Takafumi Kanamori · 2012
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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On bayesian inference for the m/g/1 queue with efficient mcmc sampling
Alexander Y Shestopaloff and Radford M Neal · 2014
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Approximating likelihood ratios with calibrated discriminative classifiers
Kyle Cranmer, Juan Pavez, and Gilles Louppe · 2015
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Likelihood-free inference by ratio estimation
Owen Thomas, Ritabrata Dutta, Jukka Corander, Samuel Kaski, Michael U Gutmann, et al · 2016
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Fast ε \varepsilon -free inference of simulation models with bayesian conditional density estimation
George Papamakarios and Iain Murray · 2016
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A general framework for updating belief distributions
Pier Giovanni Bissiri, Chris C Holmes, and Stephen G Walker · 2016
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Flexible statistical inference for mechanistic models of neural dynamics
Jan-Matthis Lueckmann, Pedro J Goncalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, and Jakob H Macke · 2017
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Inconsistency of bayesian inference for misspecified linear models, and a proposal for repairing it
Peter Grünwald and Thijs Van Ommen · 2017
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Assigning a value to a power likelihood in a general bayesian model
Chris C Holmes and Stephen G Walker · 2017
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“Active Sciencing” with Reusable Workflows
Kyle Cranmer, Lukas Heinrich, Tim Head, and Gilles Louppe · 2017
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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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Robust bayesian inference via coarsening
Jeffrey W Miller and David B Dunson · 2018
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LIGO Algorithm Library - LALSuite
LIGO Scientific Collaboration · 2018
Confidence sets and hypothesis testing in a likelihood-free inference setting
Niccolò Dalmasso, Rafael Izbicki, and Ann Lee · 2020
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Generalized posteriors in approximate bayesian computation
Sebastian M Schmon, Patrick W Cannon, and Jeremias Knoblauch · 2020
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Averting A Crisis In Simulation-Based Inference
Joeri Hermans, Arnaud Delaunoy, François Rozet, Antoine Wehenkel, and Gilles Louppe · 2021
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Variational methods for simulation-based inference
Manuel Glöckler, Michael Deistler, and Jakob H Macke · 2021
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Truncated marginal neural ratio estimation
Benjamin K Miller, Alex Cole, Patrick Forré, Gilles Louppe, and Christoph Weniger · 2021
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Cited alongside, same era.
Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows
George Papamakarios, David Sterratt, and Iain Murray · 2019
Cited alongside, same era.
Automatic posterior transformation for likelihood-free inference
David Greenberg, Marcel Nonnenmacher, and Jakob Macke · 2019
Cited alongside, same era.
PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals
C. M. Biwer, Collin D. Capano, Soumi De, Miriam Cabero, Duncan A. Brown, Alexander H. Nitz, and V. Raymond · 2019
Cited alongside, same era.
Likelihood-free MCMC with amortized approximate ratio estimators
Joeri Hermans, Volodimir Begy, and Gilles Louppe · 2020
Cited alongside, same era.
The frontier of simulation-based inference
Kyle Cranmer, Johann Brehmer, and Gilles Louppe · 2020
Cited alongside, same era.
On contrastive learning for likelihood-free inference
Conor Durkan, Iain Murray, and George Papamakarios · 2020
Cited alongside, same era.
Diagnostics for conditional density models and bayesian inference algorithms
David Zhao, Niccolò Dalmasso, Rafael Izbicki, and Ann B Lee · 2021
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Towards constraining warm dark matter with stellar streams through neural simulation-based inference
Joeri Hermans, Nilanjan Banik, Christoph Weniger, Gianfranco Bertone, and Gilles Louppe · 2021
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Benchmarking simulation-based inference
Jan-Matthis Lueckmann, Jan Boelts, David Greenberg, Pedro Goncalves, and Jakob Macke · 2021
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Niccolo Dalmasso, David Zhao, Rafael Izbicki, and Ann B Lee · 2021
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Robust generalised bayesian inference for intractable likelihoods
Takuo Matsubara, Jeremias Knoblauch, François-Xavier Briol, Chris Oates, et al · 2021
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Score matched neural exponential families for likelihood-free inference
Lorenzo Pacchiardi and Ritabrata Dutta · 2022
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Robust bayesian inference for simulator-based models via the mmd posterior bootstrap
Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas, and François-Xavier Briol · 2022
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