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Likelihood-to-evidence ratio estimation is usually cast as either a binary (NRE-A) or a multiclass (NRE-B) classification task.
Normalizing flows for probabilistic modeling and inference
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Improving predictive inference under covariate shift by weighting the log-likelihood function
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J. H. Friedman · 2003
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E. L. Lehmann, J. P. Romano, and G. Casella · 2005
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Nested sampling for general bayesian computation
J. Skilling · 2006
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Matplotlib: A 2d graphics environment
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Adaptive approximate bayesian computation
M. A. Beaumont, J.-M. Cornuet, J.-M. Marin, and C. P. Robert · 2009
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The elements of statistical learning: data mining, inference, and prediction , volume 2
T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman · 2009
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Approximate bayesian computation scheme for parameter inference and model selection in dynamical systems
T. Toni, D. Welch, N. Strelkowa, A. Ipsen, and M. P. H. Stumpf · 2009
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Non-linear regression models for approximate bayesian computation
M. G. Blum and O. François · 2010
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
M. Gutmann and A. Hyvärinen · 2010
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Data Structures for Statistical Computing in Python
Wes McKinney · 2010
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Towards constraining warm dark matter with stellar streams through neural simulation-based inference
J. Hermans, N. Banik, C. Weniger, G. Bertone, and G. Louppe · 2011
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Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
M. U. Gutmann and A. Hyvärinen · 2012
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A fast and simple algorithm for training neural probabilistic language models
A. Mnih and Y. W. Teh · 2012
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Density ratio estimation in machine learning
M. Sugiyama, T. Suzuki, and T. Kanamori · 2012
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Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean · 2013
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High-dimensional density ratio estimation with extensions to approximate likelihood computation
R. Izbicki, A. Lee, and C. Schafer · 2014
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Approximating likelihood ratios with calibrated discriminative classifiers
K. Cranmer, J. Pavez, and G. Louppe · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Adam: A method for stochastic gradient descent
D. P. Kingma and J. L. Ba · 2015
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Bayesian optimization for likelihood-free inference of simulator-based statistical models
M. U. Gutmann, J. Corander, et al · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Jupyter notebooks - a publishing format for reproducible computational workflows
T. Kluyver, B. Ragan-Kelley, F. Pérez, B. Granger, M. Bussonnier, J. Frederic, K. Kelley, J. Hamrick, J. Grout, S. Corlay, P. Ivanov, D. Avila, S. Abdalla, C. Willing, and J. development team · 2016
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Learning in implicit generative models
S. Mohamed and B. Lakshminarayanan · 2016
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Fast ε \varepsilon -free inference of simulation models with bayesian conditional density estimation
G. Papamakarios and I. Murray · 2016
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Likelihood-free inference by ratio estimation
O. Thomas, R. Dutta, J. Corander, S. Kaski, M. U. Gutmann, et al · 2016
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Glass: A general likelihood approximate solution scheme
S. Gratton · 2017
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Mining gold from implicit models to improve likelihood-free inference
J. Brehmer, G. Louppe, J. Pavez, and K. Cranmer · 2020
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Targeted likelihood-free inference of dark matter substructure in strongly-lensed galaxies
A. Coogan, K. Karchev, and C. Weniger · 2020
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The frontier of simulation-based inference
K. Cranmer, J. Brehmer, and G. Louppe · 2020
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Confidence sets and hypothesis testing in a likelihood-free inference setting
N. Dalmasso, R. Izbicki, and A. Lee · 2020
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On contrastive learning for likelihood-free inference
C. Durkan, I. Murray, and G. Papamakarios · 2020
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Generative adversarial networks
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2020
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Revisiting classifier two-sample tests
D. Lopez-Paz and M. Oquab · 2017
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Flexible statistical inference for mechanistic models of neural dynamics
J.-M. Lueckmann, P. J. Gonçalves, G. Bassetto, K. Öcal, M. Nonnenmacher, and J. H. Macke · 2017
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Masked autoregressive flow for density estimation
G. Papamakarios, T. Pavlakou, and I. Murray · 2017
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Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology
J. Alsing, B. Wandelt, and S. Feeney · 2018
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Mine: mutual information neural estimation
M. I. Belghazi, A. Baratin, S. Rajeswar, S. Ozair, Y. Bengio, A. Courville, and R. D. Hjelm · 2018
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Constraining effective field theories with machine learning
J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez · 2018
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A likelihood-free inference framework for population genetic data using exchangeable neural networks
J. Chan, V. Perrone, J. Spence, P. Jenkins, S. Mathieson, and Y. Song · 2018
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Array programming with NumPy
C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. del R’ıo, M. Wiebe, P. Peterson, P. G’erard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, and T. E. Oliphant · 2020
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Solving high-dimensional parameter inference: marginal posterior densities & moment networks
N. Jeffrey and B. D. Wandelt · 2020
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Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time
B. K. Miller, A. Cole, G. Louppe, and C. Weniger · 2020
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pandas-dev/pandas: Pandas, Feb. 2020
T. pandas development team · 2020
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Likelihood-free approximate gibbs sampling
G. Rodrigues, D. J. Nott, and S. A. Sisson · 2020
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Adaptive mcmc for synthetic likelihoods and correlated synthetic likelihoods
U. Simola, J. Corander, and U. Picchini · 2020
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sbi: A toolkit for simulation-based inference
A. Tejero-Cantero, J. Boelts, M. Deistler, J.-M. Lueckmann, C. Durkan, P. J. Gonçalves, D. S. Greenberg, and J. H. Macke · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. Wilson, K. J. Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C. J. Carey, İ. Polat, Y. Feng, E. W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E. A. Quintero, C. R. Harris, A. M. Archibald, A. H. Ribeiro, F. Pedregosa, P. van Mulbregt, and SciPy 1.0 Contributors · 2020
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Fast and credible likelihood-free cosmology with truncated marginal neural ratio estimation
A. Cole, B. K. Miller, S. J. Witte, M. X. Cai, M. W. Grootes, F. Nattino, and C. Weniger · 2021
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Variational methods for simulation-based inference
M. Glöckler, M. Deistler, and J. H. Macke · 2021
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Averting a crisis in simulation-based inference
J. Hermans, A. Delaunoy, F. Rozet, A. Wehenkel, and G. Louppe · 2021
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Parallel gaussian process surrogate bayesian inference with noisy likelihood evaluations
M. Järvenpää, M. U. Gutmann, A. Vehtari, and P. Marttinen · 2021
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Benchmarking simulation-based inference
J.-M. Lueckmann, J. Boelts, D. Greenberg, P. Goncalves, and J. Macke · 2021
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Truncated marginal neural ratio estimation
B. K. Miller, A. Cole, P. Forré, G. Louppe, and C. Weniger · 2021
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Gatsbi: Generative adversarial training for simulation-based inference
P. Ramesh, J.-M. Lueckmann, J. Boelts, Á. Tejero-Cantero, D. S. Greenberg, P. J. Goncalves, and J. H. Macke · 2021
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seaborn: statistical data visualization
M. L. Waskom · 2021
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Statistical applications of contrastive learning
M. U. Gutmann, S. Kleinegesse, and B. Rhodes · 2022
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Simulation-based inference with the generalized kullback-leibler divergence
B. K. Miller, M. Federici, C. Weniger, and P. Forré · 2023
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