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Simulation-based inference (SBI) refers to statistical inference on stochastic models for which we can generate samples, but not compute likelihoods.
An introduction to dynamic meteorology
James R Holton · 1973
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A semi-implicit scheme for the shallow water equations for application to shelf sea modelling
Jan O Backhaus · 1983
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Inferring coalescence times from DNA sequence data
Simon Tavaré, David J. Balding, R. C. Griffiths, and Peter Donnelly · 1997
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Approximate Bayesian computation in population genetics
Mark A Beaumont, Wenyang Zhang, and David J Balding · 2002
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Markov chain Monte Carlo without likelihoods
P Marjoram, J Molitor, V Plagnol, and S Tavare · 2003
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Gaussian Processes for Machine Learning
CE. Rasmussen and CKI. Williams · 2006
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Sequential Monte Carlo without likelihoods
Scott A Sisson, Yanan Fan, and Mark M Tanaka · 2007
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Adaptive approximate Bayesian computation
Mark A Beaumont, Jean-Marie Cornuet, Jean-Michel Marin, and Christian P Robert · 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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Two problems with variational expectation maximisation for time series models , page 104–124
Richard Eric Turner and Maneesh Sahani · 2011
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Experimentally calibrated population of models predicts and explains intersubject variability in cardiac cellular electrophysiology
Oliver J Britton, Alfonso Bueno-Orovio, Karel Van Ammel, Hua Rong Lu, Rob Towart, David J Gallacher, and Blanca Rodriguez · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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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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Gps-abc: Gaussian process surrogate approximate bayesian computation, 2014
Edward Meeds and Max Welling · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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A note on approximating ABC-MCMC using flexible classifiers
Kim Cuc Pham, David J Nott, and Sanjay Chaudhuri · 2014
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Approximating likelihood ratios with calibrated discriminative classifiers
Kyle Cranmer, Juan Pavez, and Gilles Louppe · 2015
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Bayesian optimization for likelihood-free inference of simulator-based statistical models, 2015
Michael U. Gutmann and Jukka Corander · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Likelihood-free inference in high-dimensional models, 2015
Athanasios Kousathanas, Christoph Leuenberger, Jonas Helfer, Mathieu Quinodoz, Matthieu Foll, and Daniel Wegmann · 2015
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Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville · 2016
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2016
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Accurate image super-resolution using very deep convolutional networks
J. Kim, J. K. Lee, and K. M. Lee · 2016
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Learning in implicit generative models
Shakir Mohamed and Balaji Lakshminarayanan · 2016
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Fast ϵ \epsilon -free inference of simulation models with Bayesian conditional density estimation
George Papamakarios and Iain Murray · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
EMNIST: an extension of MNIST to handwritten letters, 2017
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
Cited alongside, same era.
Many paths to equilibrium: Gans do not need to decrease a divergence at every step
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M Dai, Shakir Mohamed, and Ian Goodfellow · 2017
Cited alongside, same era.
On unifying deep generative models
Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, and Eric P Xing · 2017
Cited alongside, same era.
Automatic posterior transformation for likelihood-free inference
David Greenberg, Marcel Nonnenmacher, and Jakob Macke · 2019
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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
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
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Likelihood-free approximate gibbs sampling, 2019
G. S. Rodrigues, D. J. Nott, and S. A. Sisson · 2019
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Experiment tracking with weights and biases, 2020
Lukas Biewald · 2020
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Ferenc Huszár · 2017
Cited alongside, same era.
Easy high-dimensional likelihood-free inference
Vinay Jethava and Devdatt Dubhashi · 2017
Cited alongside, same era.
Adversarial variational optimization of non-differentiable simulators
Gilles Louppe, Joeri Hermans, and Kyle Cranmer · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Adversarial variational Bayes: Unifying variational autoencoders and generative adversarial networks
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
Cited alongside, same era.
Unrolled generative adversarial networks, 2017
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein · 2017
Cited alongside, same era.
Veegan: Reducing mode collapse in gans using implicit variational learning
Akash Srivastava, Lazar Valkov, Chris Russell, Michael U Gutmann, and Charles Sutton · 2017
Cited alongside, same era.
Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, and Yoshua Bengio · 2020
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The frontier of simulation-based inference
Kyle Cranmer, Johann Brehmer, and Gilles Louppe · 2020
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On contrastive learning for likelihood-free inference
Conor Durkan, Iain Murray, and George Papamakarios · 2020
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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
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Likelihood-free MCMC with approximate likelihood ratios
Joeri Hermans, Volodimir Begy, and Gilles Louppe · 2020
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Adversarial likelihood-free inference on black-box generator
Dongjun Kim, Weonyoung Joo, Seungjae Shin, and Il-Chul Moon · 2020
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Jaimit Parikh, James Kozloski, and Viatcheslav Gurev · 2020
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Bayesflow: Learning complex stochastic models with invertible neural networks
Stefan T Radev, Ulf K Mertens, Andreas Voss, Lynton Ardizzone, and Ullrich Köthe · 2020
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Validating Bayesian Inference Algorithms with Simulation-Based Calibration
Sean Talts, Michael Betancourt, Daniel Simpson, and Aki Vehtari · 2020
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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
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Deepfakes and beyond: A survey of face manipulation and fake detection, 2020
Ruben Tolosana, Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales, and Javier Ortega-Garcia · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors · 2020
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Gatsbi: An online gtsp-based algorithm for targeted surface bridge inspection, 2020
Kevin Yu, Harnaik Dhami, Kartik Madhira, and Pratap Tokekar · 2020
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Fast and credible likelihood-free cosmology with truncated marginal neural ratio estimation, 2021
Alex Cole, Benjamin Kurt Miller, Samuel J. Witte, Maxwell X. Cai, Meiert W. Grootes, Francesco Nattino, and Christoph Weniger · 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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Gatsbi: Generative agent-centric spatio-temporal object interaction, 2021
Cheol-Hui Min, Jinseok Bae, Junho Lee, and Young Min Kim · 2021
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Robot learning from randomized simulations: A review
Fabio Muratore, Fabio Ramos, Greg Turk, Wenhao Yu, Michael Gienger, and Jan Peters · 2021
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2021
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Projected gans converge faster
Axel Sauer, Kashyap Chitta, Jens Müller, and Andreas Geiger · 2021
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Likelihood-Free Inference by Ratio Estimation
Owen Thomas, Ritabrata Dutta, Jukka Corander, Samuel Kaski, and Michael U. Gutmann · 2021
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