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We present a comparison of simulation-based inference to full, field-based analytical inference in cosmological data analysis.
https://arxiv.org/abs/1902.10159
Ntampaka, M., Avestruz, C., Boada, S., et al. 2021, The Role of Machine Learning in the Next Decade of Cosmology · 1902
Earlier work this paper cites.
https://arxiv.org/abs/1908.10590
Pan, S., Liu, M., Forero-Romero, J., et al. 2020, Cosmological parameter estimation from large-scale structure deep learning · 1908
Earlier work this paper cites.
Rao, C. R. 1945, Bulletin of the Calcutta Mathematical Society, 37, 81–89
1945
Earlier work this paper cites.
Cramér, H. 1946, Mathematical methods of statistics, by Harald Cramer, .. (The University Press)
1946
Earlier work this paper cites.
Coles, P., & Jones, B. 1991, MNRAS, 248, 1, doi: 10.1093/mnras/248.1.1
1991
Earlier work this paper cites.
Francis, P. J., Hewett, P. C., Foltz, C. B., & Chaffee, F. H. 1992, Astrophys. J. , 398, 476, doi: 10.1086/171870
1992
Earlier work this paper cites.
Connolly, A. J., Szalay, A. S., Bershady, M. A., Kinney, A. L., & Calzetti, D. 1995, Astron. J., 110, 1071, doi: 10.1086/117587
1995
Earlier work this paper cites.
http://www.jstor.org/stable/1390750
Kitagawa, G. 1996, Journal of Computational and Graphical Statistics, 5, 1 · 1996
Earlier work this paper cites.
Tegmark, M., Taylor, A. N., & Heavens, A. F. 1997, The Astrophysical Journal, 480, 22–35, doi: 10.1086/303939
1997
Earlier work this paper cites.
Eisenstein, D. J., & Hu, W. 1999, The Astrophysical Journal, 511, 5–15, doi: 10.1086/306640
1999
Earlier work this paper cites.
Heavens, A. F., Jimenez, R., & Lahav, O. 2000, Monthly Notices of the Royal Astronomical Society, 317, 965–972, doi: 10.1046/j.1365-8711.2000.03692.x
2000
Earlier work this paper cites.
Dodelson, S. 2003, Modern cosmology
2003
Earlier work this paper cites.
https://arxiv.org/abs/2005.12276
Moster, B. P., Naab, T., Lindström, M., & O’Leary, J. A. 2020, GalaxyNet: Connecting galaxies and dark matter haloes with deep neural networks and reinforcement learning in large volumes · 2005
Earlier work this paper cites.
https://arxiv.org/abs/2010.15843
Makinen, T. L., Lancaster, L., Villaescusa-Navarro, F., et al. 2020, deep21: a Deep Learning Method for 21cm Foreground Removal · 2010
Earlier work this paper cites.
https://arxiv.org/abs/1110.3193
Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, Euclid Definition Study Report · 2011
Earlier work this paper cites.
https://arxiv.org/abs/2011.05992
Villaescusa-Navarro, F., Wandelt, B. D., Anglés-Alcázar, D., et al. 2020, Neural networks as optimal estimators to marginalize over baryonic effects · 2011
Earlier work this paper cites.
https://arxiv.org/abs/2012.00240
de Oliveira, R. A., Li, Y., Villaescusa-Navarro, F., Ho, S., & Spergel, D. N. 2020, Fast and Accurate Non-Linear Predictions of Universes with Deep Learning · 2012
Earlier work this paper cites.
https://arxiv.org/abs/1211.0310
LSST Dark Energy Science Collaboration. 2012, Large Synoptic Survey Telescope: Dark Energy Science Collaboration · 2012
Earlier work this paper cites.
Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, Publications of the Astronomical Society of the Pacific, 125, 306–312, doi: 10.1086/670067
2013
Cited alongside, same era.
Jasche, J., & Wandelt, B. D. 2013, Mon. Not. Roy. Astron. Soc., 432, 894, doi: 10.1093/mnras/stt449
2013
Cited alongside, same era.
Kingma, D. P., & Ba, J. 2014, arXiv e-prints, arXiv:1412.6980
2014
Cited alongside, same era.
Planck Collaboration, Ade, P. A. R., Arnaud, M., et al. 2014, A&A, 571, A31, doi: 10.1051/0004-6361/201423743
2014
Cited alongside, same era.
Greiner, M., & Enßlin, T. A. 2015, Astronomy & Astrophysics, 574, A86, doi: 10.1051/0004-6361/201323181
2015
Cited alongside, same era.
Fluri, J., Kacprzak, T., Lucchi, A., et al. 2019, Physical Review D, 100, doi: 10.1103/physrevd.100.063514
2019
Later among the works it cites.
Gillet, N., Mesinger, A., Greig, B., Liu, A., & Ucci, G. 2019, Monthly Notices of the Royal Astronomical Society, doi: 10.1093/mnras/stz010
2019
Later among the works it cites.
He, S., Li, Y., Feng, Y., et al. 2019, Proceedings of the National Academy of Sciences, 116, 13825, doi: 10.1073/pnas.1821458116
2019
Later among the works it cites.
Jasche, J., & Lavaux, G. 2019, Astronomy & Astrophysics, 625, A64, doi: 10.1051/0004-6361/201833710
2019
Later among the works it cites.
https://arxiv.org/abs/1805.07226
Papamakarios, G., Sterratt, D. C., & Murray, I. 2019, Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows · 2019
Later among the works it cites.
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Jasche, J., Leclercq, F., & Wandelt, B. D. 2015, Journal of Cosmology and Astroparticle Physics, 2015, 036, doi: 10.1088/1475-7516/2015/01/036
2015
Cited alongside, same era.
Ade, P. A. R., Aghanim, N., Arnaud, M., et al. 2016, Astronomy & Astrophysics, 594, A13, doi: 10.1051/0004-6361/201525830
2016
Cited alongside, same era.
Lavaux, G., & Jasche, J. 2016, Mon. Not. Roy. Astron. Soc., 455, 3169, doi: 10.1093/mnras/stv2499
2016
Cited alongside, same era.
https://arxiv.org/abs/1602.07261
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. 2016, Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning · 2016
Cited alongside, same era.
Heavens, A. F., Sellentin, E., de Mijolla, D., & Vianello, A. 2017, Monthly Notices of the Royal Astronomical Society, 472, 4244–4250, doi: 10.1093/mnras/stx2326
2017
Cited alongside, same era.
Lakshminarayanan, B., Pritzel, A., & Blundell, C. 2017, in Advances in Neural Information Processing Systems 30, ed. I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Curran Associates, Inc.), 6402–6413
2017
Cited alongside, same era.
https://proceedings.neurips.cc/paper/2017/file/6c1da886822c67822bcf3679d04369fa-Paper.pdf
Papamakarios, G., Pavlakou, T., & Murray, I. 2017, in Advances in Neural Information Processing Systems, ed. I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett, Vol. 30 (Curran Associates, Inc.) · 2017
Cited alongside, same era.
Weltman, A., Bull, P., Camera, S., et al. 2020, Publications of the Astronomical Society of Australia, 37, doi: 10.1017/pasa.2019.42
2019
Later among the works it cites.
https://github.com/DifferentiableUniverseInitiative/jax-cosmo-paper
Casas, S., & Lanusse, F. 2020, jax-cosmo, Github · 2020
Later among the works it cites.
Cranmer, K., Brehmer, J., & Louppe, G. 2020, Proceedings of the National Academy of Sciences, 117, 30055, doi: 10.1073/pnas.1912789117
2020
Later among the works it cites.
Grazian, C., & Fan, Y. 2020, Wiley Interdisciplinary Reviews: Computational Statistics, 12, e1486
2020
Later among the works it cites.
Kodi Ramanah, D., Charnock, T., Villaescusa-Navarro, F., & Wandelt, B. D. 2020, Monthly Notices of the Royal Astronomical Society, 495, 4227–4236, doi: 10.1093/mnras/staa1428
2020
Later among the works it cites.
Kwon, Y., Hong, S. E., & Park, I. 2020, Journal of the Korean Physical Society, 77, 49–59, doi: 10.3938/jkps.77.49
2020
Later among the works it cites.
Matilla, J. M. Z., Sharma, M., Hsu, D., & Haiman, Z. 2020, Physical Review D, 102, doi: 10.1103/physrevd.102.123506
2020
Later among the works it cites.
Petroff, M. A., Addison, G. E., Bennett, C. L., & Weiland, J. L. 2020, The Astrophysical Journal, 903, 104, doi: 10.3847/1538-4357/abb9a7
2020
Later among the works it cites.
Puglisi, G., & Bai, X. 2020, The Astrophysical Journal, 905, 143, doi: 10.3847/1538-4357/abc47c
2020
Later among the works it cites.
https://arxiv.org/abs/2103.04158
Leclercq, F., & Heavens, A. 2021, On the accuracy and precision of correlation functions and field-level inference in cosmology · 2021
Closest in time.
https://arxiv.org/abs/2102.01086
Livet, F., Charnock, T., Borgne, D. L., & de Lapparent, V. 2021, Catalog-free modeling of galaxy types in deep images: Massive dimensional reduction with neural networks · 2021
Closest in time.
https://arxiv.org/abs/2107.00018
Prelogović, D., Mesinger, A., Murray, S., Fiameni, G., & Gillet, N. 2021, Machine learning galaxy properties from 21 cm lightcones: impact of network architectures and signal contamination · 2021
Closest in time.