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We present algorithms (a) for nested neural likelihood-to-evidence ratio estimation, and (b) for simulation reuse via an inhomogeneous Poisson point process cache of parameters and corresponding simulations.
Poisson Processes
J. F. C. Kingman · 1993
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Nested sampling for general bayesian computation
John Skilling · 2006
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Matplotlib: A 2d graphics environment
J. D. Hunter · 2007
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MultiNest: an efficient and robust bayesian inference tool for cosmology and particle physics
F Feroz, M P Hobson, and M Bridges · 2008
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X-ray spectral modelling of the agn obscuring region in the cdfs: Bayesian model selection and catalogue
Buchner, J., Georgakakis, A., Nandra, K., Hsu, L., Rangel, C., Brightman, M., Merloni, A., Salvato, M., Donley, J., and Kocevski, D · 2014
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polychord : next-generation nested sampling
W J Handley, M P Hobson, and A N Lasenby · 2015
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Approximating likelihood ratios with calibrated discriminative classifiers
Kyle Cranmer, Juan Pavez, and Gilles Louppe · 2015
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Strong support for the millisecond pulsar origin of the galactic center gev excess
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Halo and subhalo demographics with planck cosmological parameters: Bolshoi–planck and multidark–planck simulations
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Jupyter notebooks - a publishing format for reproducible computational workflows
Thomas Kluyver, Benjamin Ragan-Kelley, Fernando Pérez, Brian Granger, Matthias Bussonnier, Jonathan Frederic, Kyle Kelley, Jessica Hamrick, Jason Grout, Sylvain Corlay, Paul Ivanov, Damián Avila, Safia Abdalla, Carol Willing, and Jupyter development team · 2016
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Probing the nature of dark matter particles with stellar streams
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Handbook of approximate Bayesian computation
Scott A Sisson, Yanan Fan, and Mark Beaumont · 2018
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Likelihood-free mcmc with amortized approximate ratio estimators
Joeri Hermans, Volodimir Begy, and Gilles Louppe · 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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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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Targeted likelihood-free inference of dark matter substructure in strongly-lensed galaxies
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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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Automatic posterior transformation for likelihood-free inference
David Greenberg, Marcel Nonnenmacher, and Jakob Macke · 2019
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Adam Coogan, Konstantin Karchev, and Christoph Weniger · 2020
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Array programming with NumPy
Charles R. Harris, K. Jarrod Millman, St’efan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fern’andez del R’ıo, Mark Wiebe, Pearu Peterson, Pierre G’erard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant · 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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