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We combine amortized neural posterior estimation with importance sampling for fast and accurate gravitational-wave inference.
1904
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A. B. Owen, Monte Carlo theory, methods and examples (2013)
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J. Veitch and W. Del Pozzo, Analytic marginalisation of phase parameter, URL: https://dcc. ligo. org/LIGO-T1300326/public (2013)
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M. Hannam, P. Schmidt, A. Bohé, L. Haegel, S. Husa, F. Ohme, G. Pratten, and M. Pürrer, Simple model of complete precessing black-hole-binary gravitational waveforms, Phys. Rev. Lett. 113
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LIGO Scientific Collaboration, LIGO Algorithm Library - LALSuite , free software (GPL) (2018)
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T. Müller, B. McWilliams, F. Rousselle, M. Gross, and J. Novák, Neural importance sampling, ACM Transactions on Graphics (TOG) 38
2019
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F. Noé, S. Olsson, J. Köhler, and H. Wu, Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning, Science 365
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J. Aasi et al. (LIGO Scientific), Advanced LIGO, Class. Quant. Grav. 32
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2019
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K. Cranmer, J. Brehmer, and G. Louppe, The frontier of simulation-based inference, Proceedings of the National Academy of Sciences 117
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C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios, nflows: normalizing flows in PyTorch (2020)
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2021
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G. Papamakarios, E. T. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan, Normalizing flows for probabilistic modeling and inference., J. Mach. Learn. Res. 22
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2022
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H. Sun, K. L. Bouman, P. Tiede, J. J. Wang, S. Blunt, and D. Mawet, α \alpha -deep probabilistic inference ( α \alpha -dpi): Efficient uncertainty quantification from exoplanet astrometry to black hole feature extraction, The Astrophysical Journal 932
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2022
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