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Several rapid parameter estimation methods have recently been advanced to deal with the computational challenges of the problem of Bayesian inference of the properties of compact binary sources detected in the upcoming science runs of the terrestrial network of gravitational wave detectors.
J. Skilling, Nested sampling for general Bayesian computation, Bayesian Anal. 1
2006
Earlier work this paper cites.
G. E. Fasshauer, Meshfree Approximation Methods with Matlab (World Scientific, 2007)
2007
Earlier work this paper cites.
M. Vallisneri, Use and abuse of the Fisher information matrix in the assessment of gravitational-wave parameter-estimation prospects, Phys. Rev. D 77
2008
Earlier work this paper cites.
K. Cannon, A. Chapman, C. Hanna, D. Keppel, A. C. Searle, and A. J. Weinstein, Singular value decomposition applied to compact binary coalescence gravitational-wave signals, Phys. Rev. D 82
2010
Earlier work this paper cites.
G. M. Manca and M. Vallisneri, Cover art: Issues in the metric-guided and metric-less placement of random and stochastic template banks, Phys. Rev. D 81
2010
Earlier work this paper cites.
C. P. Ayanendranath Basu, Hiroyuki Shioya, Statistical Inference: The Minimum Distance Approach , 1st ed. (Chapman and Hall/CRC, 2011)
2011
Earlier work this paper cites.
B. Allen, W. G. Anderson, P. R. Brady, D. A. Brown, and J. D. E. Creighton, Findchirp: An algorithm for detection of gravitational waves from inspiraling compact binaries, Phys. Rev. D 85
2012
Earlier work this paper cites.
G. H. Golub and C. F. Van Loan, Matrix Computations , Fourth ed. (The Johns Hopkins University Press, 2013)
2013
Earlier work this paper cites.
D. Foreman-Mackey, D. W. Hogg, D. Lang, and J. Goodman, emcee
2013
Earlier work this paper cites.
R. Smith, C. Hanna, I. Mandel, and A. Vecchio, Rapidly evaluating the compact-binary likelihood function via interpolation, Phys. Rev. D 90
2014
Earlier work this paper cites.
P. Canizares, S. E. Field, J. Gair, V. Raymond, R. Smith, and M. Tiglio, Accelerated gravitational wave parameter estimation with reduced order modeling, Phy. Rev. Lett. 114
2015
Earlier work this paper cites.
T. Hines, Python package containing the tools necessary for radial basis function (RBF) applications (2015)
2015
Earlier work this paper cites.
C. Pankow, P. Brady, E. Ochsner, and R. O’Shaughnessy, Novel scheme for rapid parallel parameter estimation of gravitational waves from compact binary coalescences, Phys. Rev. D 92
2015
Cited alongside, same era.
L. P. Singer and L. R. Price, Rapid Bayesian position reconstruction for gravitational-wave transients, Phys. Rev. D 93
2016
Cited alongside, same era.
S. A. Usman, A. H. Nitz, I. W. Harry, C. M. Biwer, D. A. Brown, M. Cabero, C. D. Capano, T. Dal Canton, T. Dent, S. Fairhurst, et al. , The PyCBC search for gravitational waves from compact binary coalescence, Class. Quantum Gravity 33
2016
Cited alongside, same era.
S. Khan, S. Husa, M. Hannam, F. Ohme, M. Pürrer, X. J. Forteza, and A. Bohé, Frequency-domain gravitational waves from nonprecessing black-hole binaries. II. A phenomenological model for the advanced detector era, Phys. Rev. D 93
2016
Cited alongside, same era.
D. Finstad and D. A. Brown, Fast parameter estimation of binary mergers for multimessenger follow-up, Astrophys. J. Lett. 905
2020
Later among the works it cites.
B. P. Abbott et al. (KAGRA, LIGO Scientific and Virgo Collaboration), Prospects for observing and localizing gravitational-wave transients with Advanced LIGO, Advanced Virgo and KAGRA, Living Rev. Relativ. 23
2020
Later among the works it cites.
S. Morisaki and V. Raymond, Rapid parameter estimation of gravitational waves from binary neutron star coalescence using focused reduced order quadrature, Phys. Rev. D 102
2020
Later among the works it cites.
A. J. Chua and M. Vallisneri, Learning Bayesian posteriors with neural networks for gravitational-wave inference, Phys. Rev. Lett. 124
2020
Later among the works it cites.
J. S. Speagle, DYNESTY
2020
Later among the works it cites.
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B. P. Abbott et al. (LIGO Scientific, Virgo, Fermi
2017
Cited alongside, same era.
C. Messick, K. Blackburn, P. Brady, P. Brockill, K. Cannon, R. Cariou, S. Caudill, S. J. Chamberlin, J. D. Creighton, R. Everett, et al. , Analysis framework for the prompt discovery of compact binary mergers in gravitational-wave data, Phys. Rev. D 95
2017
Cited alongside, same era.
J. Lange, R. O’Shaughnessy, and M. Rizzo, Rapid and accurate parameter inference for coalescing, precessing compact binaries (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
L. Barsotti, S. Gras, M. Evans, and P. Fritschel, The updated Advanced LIGO design curve , Tech. Rep. LIGO-T1800044-v5 (LIGO Scientific Collaboration, 2018)
2018
Cited alongside, same era.
B. Margalit and B. D. Metzger, The multi-messenger matrix: The future of neutron star merger constraints on the nuclear equation of state, Astrophys. J. Lett. 880
2019
Cited alongside, same era.
E. Thrane and C. Talbot, An introduction to Bayesian inference in gravitational-wave astronomy: parameter estimation, model selection, and hierarchical models, Publications of the Astronomical Society of Australia 36
2019
Cited alongside, same era.
C. M. Biwer, C. D. Capano, S. De, M. Cabero, D. A. Brown, A. H. Nitz, and V. Raymond, PyCBC Inference: a Python-based parameter estimation toolkit for compact binary coalescence signals, Publ. Astron. Soc. Pac. 131
2019
Cited alongside, same era.
M. Dax, S. R. Green, J. Gair, J. H. Macke, A. Buonanno, and B. Schölkopf, Real-time gravitational wave science with neural posterior estimation, Phys. Rev. Lett. 127
2021
Later among the works it cites.
H. Gabbard, C. Messenger, I. S. Heng, F. Tonolini, and R. Murray-Smith, Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy, Nature Physics 18
2021
Later among the works it cites.
H. Qi and V. Raymond, Python-based reduced order quadrature building code for fast gravitational wave inference, Phys. Rev. D 104
2021
Later among the works it cites.
N. J. Cornish, Heterodyned likelihood for rapid gravitational wave parameter inference, Phys. Rev. D 104
2021
Later among the works it cites.
M. Dax, S. R. Green, J. Gair, M. Pürrer, J. Wildberger, J. H. Macke, A. Buonanno, and B. Schölkopf, Neural importance sampling for rapid and reliable gravitational-wave inference, Phys. Rev. Lett. 130
2023
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S. Koposov, J. Speagle, K. Barbary, G. Ashton, E. Bennett, J. Buchner, C. Scheffler, B. Cook, C. Talbot, J. Guillochon, et al. , joshspeagle/dynesty: v2.1.0 (2023)
2023
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L. Pathak, S. Munishwar, A. Reza, and A. S. Sengupta, Prompt sky localization of EM counterparts of GW transients using meshfree approximations (in preparation (2023))
2023
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