Fetching the paper…
Reading the bibliography…
Bayesian inference is often used in cosmology and astrophysics to derive constraints on model parameters from observations.
P. C. Mahalanobis, On the generalized distance in statistics, Proceedings of the National Institute of Sciences (Calcutta) 2 (1936) 49–55
1936
Earlier work this paper cites.
S. Kullback, R. A. Leibler, On information and sufficiency, The Annals of Mathematical Statistics (1951) 79–86
1951
Earlier work this paper cites.
N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, E. Teller, Equation of State Calculations by Fast Computing Machines, J. Chem. Phys. 21 (6) (1953) 1087
1953
Earlier work this paper cites.
W. K. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika 57 (1) (1970) 97
1970
Earlier work this paper cites.
S. Geman, D. Geman, Stochastic relaxation, gibbs distributions, and the bayesian restoration of images, Pattern Analysis and Machine Intelligence, IEEE Transactions on (6) (1984) 721–741
1984
Earlier work this paper cites.
S. Duane, A. D. Kennedy, B. J. Pendleton, D. Roweth, Hybrid monte carlo, Physics letters B 195 (2) (1987) 216–222
1987
Earlier work this paper cites.
P. Del Moral, Non-linear filtering: interacting particle resolution, Markov processes and related fields 2 (4) (1996) 555–581
1996
Earlier work this paper cites.
E. Bertin, S. Arnouts, Sextractor: Software for source extraction., Astronomy and Astrophysics Supplement Series 117 (1996) 393–404
1996
Earlier work this paper cites.
J. K. Pritchard, M. T. Seielstad, A. Perez-Lezaun, M. W. Feldman, Population growth of human y chromosomes: a study of y chromosome microsatellites., Molecular Biology and Evolution 16 (12) (1999) 1791–1798
1999
Earlier work this paper cites.
N. Christensen, R. Meyer, L. Knox, B. Luey, Bayesian methods for cosmological parameter estimation from cosmic microwave background measurements, Class. Quant. Grav. 18 (14) (2001) 2677
2001
Earlier work this paper cites.
L. Knox, N. Christensen, C. Skordis, The Age of the Universe and the Cosmological Constant Determined from Cosmic Microwave Background Anisotropy Measurements, ApJ 563 (2) (2001) L95
2001
Earlier work this paper cites.
A. Lewis, S. Bridle, Cosmological parameters from cmb and other data: A monte carlo approach, Physical Review D 66 (10) (2002) 103511
2002
Earlier work this paper cites.
P. Marjoram, J. Molitor, V. Plagnol, S. Tavaré, Markov chain monte carlo without likelihoods, Proceedings of the National Academy of Sciences 100 (26) (2003) 15324–15328
2003
Earlier work this paper cites.
J. Skilling, Nested sampling, Bayesian inference and maximum entropy methods in science and engineering 735 (2004) 395–405
2004
Earlier work this paper cites.
M. Reinecke, K. Dolag, R. Hell, M. Bartelmann, T. Enßlin, A simulation pipeline for the planck mission, Astronomy & Astrophysics 445 (1) (2006) 373–373
2006
Earlier work this paper cites.
C. Heymans, L. Van Waerbeke, D. Bacon, J. Berge, G. Bernstein, E. Bertin, S. Bridle, M. L. Brown, D. Clowe, H. Dahle, et al., The shear testing programme–i. weak lensing analysis of simulated ground-based observations, Monthly Notices of the Royal Astronomical Society 368 (3) (2006) 1323–1339
2006
Earlier work this paper cites.
J. Juin, D. Yvon, A. Réfrégier, C. Yeche, Cosmology with wide-field sz cluster surveys: selection and systematic effects, Astronomy & Astrophysics 465 (1) (2007) 57–65
2007
Earlier work this paper cites.
R. Massey, C. Heymans, J. Bergé, G. Bernstein, S. Bridle, D. Clowe, H. Dahle, R. Ellis, T. Erben, M. Hetterscheidt, et al., The shear testing programme 2: Factors affecting high-precision weak-lensing analyses, Monthly Notices of the Royal Astronomical Society 376 (1) (2007) 13–38
2007
Earlier work this paper cites.
S. A. Sisson, Y. Fan, M. M. Tanaka, Sequential monte carlo without likelihoods, Proceedings of the National Academy of Sciences 104 (6) (2007) 1760–1765
2007
Earlier work this paper cites.
F. Feroz, M. Hobson, M. Bridges, Multinest: an efficient and robust bayesian inference tool for cosmology and particle physics, Monthly Notices of the Royal Astronomical Society 398 (4) (2009) 1601–1614
2009
Cited alongside, same era.
S. Pires, J.-L. Starck, A. Amara, R. Teyssier, A. Réfrégier, J. Fadili, Fast statistics for weak lensing (fastlens): fast method for weak lensing statistics and map making, Monthly Notices of the Royal Astronomical Society 395 (3) (2009) 1265–1279
2009
Cited alongside, same era.
S. Bridle, J. Shawe-Taylor, A. Amara, D. Applegate, S. T. Balan, J. Berge, G. Bernstein, H. Dahle, T. Erben, M. Gill, et al., Handbook for the great08 challenge: An image analysis competition for cosmological lensing, The Annals of Applied Statistics (2009) 6–37
2009
Cited alongside, same era.
T. Toni, D. Welch, N. Strelkowa, A. Ipsen, M. P. Stumpf, Approximate bayesian computation scheme for parameter inference and model selection in dynamical systems, Journal of the Royal Society Interface 6 (31) (2009) 187–202
C. M. Schafer, P. E. Freeman, Likelihood-free inference in cosmology: Potential for the estimation of luminosity functions, in: Statistical Challenges in Modern Astronomy V, Springer, 2012, pp. 3–19
2012
Later among the works it cites.
D. Foreman-Mackey, D. W. Hogg, D. Lang, J. Goodman, emcee: The mcmc hammer, Publications of the Astronomical Society of the Pacific 125 (925) (2013) 306–312
2013
Later among the works it cites.
J. Akeret, S. Seehars, A. Amara, A. Refregier, A. Csillaghy, Cosmohammer: Cosmological parameter estimation with the mcmc hammer, Astronomy and Computing 2 (2013) 27–39
2013
Later among the works it cites.
A. Weyant, C. Schafer, W. M. Wood-Vasey, Likelihood-free cosmological inference with type ia supernovae: approximate bayesian computation for a complete treatment of uncertainty, The Astrophysical Journal 764 (2) (2013) 116
2013
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2009
Cited alongside, same era.
M. A. Beaumont, J.-M. Cornuet, J.-M. Marin, C. P. Robert, Adaptive approximate bayesian computation, Biometrika (2009) asp052
2009
Cited alongside, same era.
D. Wraith, M. Kilbinger, K. Benabed, O. Cappé, J.-F. Cardoso, G. Fort, S. Prunet, C. P. Robert, Estimation of cosmological parameters using adaptive importance sampling, Physical Review D 80 (2) (2009) 023507
2009
Cited alongside, same era.
D. W. Scott, Multivariate density estimation: theory, practice, and visualization, Vol. 383, John Wiley & Sons, 2009
2009
Cited alongside, same era.
J. Goodman, J. Weare, Ensemble samplers with affine invariance, Comm. App. Math. Comp. Sci. 5 (1) (2010) 65
2010
Cited alongside, same era.
J. Dietrich, J. Hartlap, Cosmology with the shear-peak statistics, Monthly Notices of the Royal Astronomical Society 402 (2) (2010) 1049–1058
2010
Cited alongside, same era.
M. A. Beaumont, Approximate bayesian computation in evolution and ecology, Annual review of ecology, evolution, and systematics 41 (2010) 379–406
2010
Cited alongside, same era.
K. Csilléry, M. G. Blum, O. E. Gaggiotti, O. François, Approximate bayesian computation (abc) in practice, Trends in ecology & evolution 25 (7) (2010) 410–418
2010
Cited alongside, same era.
A. Mesinger, S. Furlanetto, R. Cen, 21cmfast: a fast, seminumerical simulation of the high-redshift 21-cm signal, Monthly Notices of the Royal Astronomical Society 411 (2) (2011) 955–972
2011
Cited alongside, same era.
J. Bergé, L. Gamper, A. Réfrégier, A. Amara, An ultra fast image generator (ufig) for wide-field astronomy, Astronomy and Computing 1 (2013) 23–32
2013
Later among the works it cites.
M. Lenormand, F. Jabot, G. Deffuant, Adaptive approximate bayesian computation for complex models, Computational Statistics 28 (6) (2013) 2777–2796
2013
Later among the works it cites.
S. Filippi, C. P. Barnes, J. Cornebise, M. P. Stumpf, On optimality of kernels for approximate bayesian computation using sequential monte carlo, Statistical applications in genetics and molecular biology 12 (1) (2013) 87–107
2013
Later among the works it cites.
M. G. Blum, M. A. Nunes, D. Prangle, S. A. Sisson, et al., A comparative review of dimension reduction methods in approximate bayesian computation, Statistical Science 28 (2) (2013) 189–208
2013
Later among the works it cites.
G. Aslanyan, Cosmo++: An object-oriented c++ library for cosmology, Computer Physics Communications 185 (12) (2014) 3215–3227
2014
Later among the works it cites.
A. Refregier, A. Amara, A way forward for cosmic shear: Monte-carlo control loops, Physics of the Dark Universe 3 (2014) 1–3
2014
Later among the works it cites.
J. Liepe, P. Kirk, S. Filippi, T. Toni, C. P. Barnes, M. P. Stumpf, A framework for parameter estimation and model selection from experimental data in systems biology using approximate bayesian computation, Nature protocols 9 (2) (2014) 439–456
2014
Later among the works it cites.
A. Robin, C. Reylé, J. Fliri, M. Czekaj, C. Robert, A. Martins, Constraining the thick disc formation scenario of the milky way, Astronomy & Astrophysics 569 (2014) A13
2014
Later among the works it cites.
H. Diehl, T. Abbott, J. Annis, R. Armstrong, L. Baruah, A. Bermeo, G. Bernstein, E. Beynon, C. Bruderer, E. Buckley-Geer, et al., The dark energy survey and operations: Year 1, in: SPIE Astronomical Telescopes+ Instrumentation, International Society for Optics and Photonics, 2014, pp. 91490V–91490V
2014
Later among the works it cites.
W. Handley, M. Hobson, A. Lasenby, polychord: nested sampling for cosmology, Monthly Notices of the Royal Astronomical Society: Letters 450 (1) (2015) L61–L65
2015
Closest in time.
C. Chang, M. T. Busha, R. H. Wechsler, A. Refregier, A. Amara, E. Rykoff, M. R. Becker, C. Bruderer, L. Gamper, B. Leistedt, H. Peiris, T. Abbott, F. B. Abdalla, E. Balbinot, M. Banerji, R. A. Bernstein, E. Bertin, D. Brooks, A. Carnero, S. Desai, L. N. da Costa, C. E. Cunha, T. Eifler, A. E. Evrard, A. Fausti Neto, D. Gerdes, D. Gruen, D. James, K. Kuehn, M. A. G. Maia, M. Makler, R. Ogando, A. Plazas, E. Sanchez, B. Santiago, M. Schubnell, I. Sevilla-Noarbe, C. Smith, M. Soares-Santos, E. Suchyta, M. E. C. Swanson, G. Tarle, J. Zuntz, Modeling the Transfer Function for the Dark Energy Survey, ApJ 801 (2015) 73 · 2015
Closest in time.
F. V. Bonassi, M. West, et al., Sequential monte carlo with adaptive weights for approximate bayesian computation, Bayesian Analysis 10 (1) (2015) 171–187
2015
Closest in time.
S. Barber, J. Voss, M. Webster, et al., The rate of convergence for approximate bayesian computation, Electronic Journal of Statistics 9 (2015) 80–105
2015
Closest in time.
M. A. Beaumont, W. Zhang, D. J. Balding, Approximate bayesian computation in population genetics, Genetics 162 (4) (2002) 2025–2035
2035
Closest in time.