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Building on the recent trend of new deep generative models known as Normalizing Flows (NF), simulation-based inference (SBI) algorithms can now efficiently accommodate arbitrary complex and high-dimensional data distributions.
Likelihood-free MCMC with amortized approximate ratio estimators
Joeri Hermans, Volodimir Begy, and Gilles Louppe · 1903
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 1912
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On loss functions which minimii to conditional expected values and posterior probabilities
John W. Miller, Rod Goodman, and Padhraic Smyth · 1993
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Multiple significance tests: the bonferroni method
J. Martin Bland and Douglas G. Altman · 1995
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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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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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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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The frontier of simulation-based inference
Kyle Cranmer, Johann Brehmer, and Gilles Louppe · 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
Cited alongside, same era.
Sbi – a toolkit for simulation-based inference
Álvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro J Gonçalves, David S Greenberg, Jakob H Macke, Computational Neuroengineering, and T U Munich · 2020
Cited alongside, same era.
Likelihood-free frequentist inference: Confidence sets with correct conditional coverage
Niccolò Dalmasso, David Zhao, Rafael Izbicki, and Ann B. Lee · 2021
Later among the works it cites.
Benchmarking simulation-based inference
Jan-Matthis Lueckmann, Jan Boelts, David S Greenberg, Pedro J Gonçalves, and Jakob H Macke · 2021
Later among the works it cites.
Hnpe: Leveraging global parameters for neural posterior estimation
Pedro L C Rodrigues, Thomas Moreau, and Gilles Louppe · 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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Simulation-based inference with waldo: Perfectly calibrated confidence regions using any prediction or posterior estimation algorithm
Luca Masserano, Tommaso Dorigo, Rafael Izbicki, Mikael Kuusela, and Ann B. Lee · 2022
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Interrogating theoretical models of neural computation with emergent property inference
Sean R. Bittner, Agostina Palmigiano, Alex T. Piet, Chunyu A. Duan, Carlos D. Brody, Kenneth D. Miller, and John Cunningham · 2021
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Flexible and efficient simulation-based inference for models of decision-making
Jan Boelts, Jan-Matthis Lueckmann, Richard Gao, and Jakob H. Macke · 2021
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ADAVI: Automatic Dual Amortized Variational Inference Applied To Pyramidal Bayesian Models
Louis Rouillard and Demian Wassermann · 2022
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