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We present extensive empirical evidence showing that current Bayesian simulation-based inference algorithms can produce computationally unfaithful posterior approximations.
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Approximate bayesian computation in population genetics
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Markov chain monte carlo without likelihoods
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Observation of a new particle in the search for the standard model higgs boson with the atlas detector at the lhc
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Paul Fearnhead and Dennis Prangle · 2012
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Robert Schall · 2012
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Masashi Sugiyama, Taiji Suzuki, and Takafumi Kanamori · 2012
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Bounding the test log-likelihood of generative models
Yoshua Bengio, Li Yao, and Kyunghyun Cho · 2014
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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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An integrated procedure for bayesian reliability inference using mcmc
Jing Lin · 2014
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Dennis Prangle, Michael GB Blum, G Popovic, and SA Sisson · 2014
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Approximating likelihood ratios with calibrated discriminative classifiers
Kyle Cranmer, Juan Pavez, and Gilles Louppe · 2015
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Laurent Dinh, David Krueger, and Yoshua Bengio · 2015
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Gintare Karolina Dziugaite, Daniel M Roy, and Zoubin Ghahramani · 2015
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Variational inference with normalizing flows
Validating bayesian inference algorithms with simulation-based calibration
Sean Talts, Michael Betancourt, Daniel Simpson, Aki Vehtari, and Andrew Gelman · 2018
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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
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Mining for dark matter substructure: Inferring subhalo population properties from strong lenses with machine learning
Johann Brehmer, Siddharth Mishra-Sharma, Joeri Hermans, Gilles Louppe, and Kyle Cranmer · 2019
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Automatic posterior transformation for likelihood-free inference
David Greenberg, Marcel Nonnenmacher, and Jakob Macke · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Danilo Rezende and Shakir Mohamed · 2015
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Gw151226: observation of gravitational waves from a 22-solar-mass binary black hole coalescence
Benjamin P Abbott, R Abbott, TD Abbott, MR Abernathy, F Acernese, K Ackley, C Adams, T Adams, P Addesso, RX Adhikari, 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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Likelihood-free inference by ratio estimation
Owen Thomas, Ritabrata Dutta, Jukka Corander, Samuel Kaski, Michael U Gutmann, et al · 2016
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“Active Sciencing” with Reusable Workflows
Kyle Cranmer, Lukas Heinrich, Tim Head, and Gilles Louppe · 2017
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Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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Mcmc diagnostics for higher dimensions using kullback leibler divergence
Anand Dixit and Vivekananda Roy · 2017
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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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The frontier of simulation-based inference
Kyle Cranmer, Johann Brehmer, and Gilles Louppe · 2020
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Confidence sets and hypothesis testing in a likelihood-free inference setting
Niccolò Dalmasso, Rafael Izbicki, and Ann Lee · 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, et al · 2020
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Likelihood-free MCMC with amortized approximate ratio estimators
Joeri Hermans, Volodimir Begy, and Gilles Louppe · 2020
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Convergence diagnostics for markov chain monte carlo
Vivekananda Roy · 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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sbi: A toolkit for simulation-based inference
Alvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro Gonçalves, David Greenberg, and Jakob Macke · 2020
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Distortion estimates for approximate bayesian inference
Hanwen Xing, Geoff Nicholls, and Jeong Kate Lee · 2020
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Planck 2018 results. VI. Cosmological parameters
N. Aghanim et al · 2021
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Niccolò Dalmasso, David Zhao, 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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Diagnostics for conditional density models and bayesian inference algorithms
David Zhao, Niccolò Dalmasso, Rafael Izbicki, and Ann B Lee · 2021
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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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Bayesian inference using synthetic likelihood: asymptotics and adjustments
David T Frazier, David J Nott, Christopher Drovandi, and Robert Kohn · 2022
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Robust generalised bayesian inference for intractable likelihoods
Takuo Matsubara, Jeremias Knoblauch, François-Xavier Briol, and Chris J Oates · 2022
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Score matched neural exponential families for likelihood-free inference
Lorenzo Pacchiardi and Ritabrata Dutta · 2022
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