Fetching the paper…
Reading the bibliography…
Weak gravitational lensing is one of the few direct methods to map the dark-matter distribution on large scales in the Universe, and to estimate cosmological parameters.
Hotelling, H., 08 1931. The generalization of student’s ratio. Ann. Math. Statist. 2 (3), 360–378. URL https://doi.org/10.1214/aoms/1177732979
1931
Earlier work this paper cites.
Taylor, A., Joachimi, B., Kitching, T., Jul. 2013. Putting the precision in precision cosmology: How accurate should your data covariance matrix be? MNRAS 432, 1928–1946
1946
Earlier work this paper cites.
Limber, D. N., Jan. 1953. The Analysis of Counts of the Extragalactic Nebulae in Terms of a Fluctuating Density Field. ApJ 117, 134–+
1953
Earlier work this paper cites.
Olkin, I., Roy, S. N., 06 1954. On multivariate distribution theory. Ann. Math. Statist. 25 (2), 329–339. URL https://doi.org/10.1214/aoms/1177728789
1954
Earlier work this paper cites.
Zel’Dovich, Y. B., Mar. 1970. Reprint of 1970A&A…..5…84Z. Gravitational instability: an approximate theory for large density perturbations. A&A 500, 13–18
1970
Earlier work this paper cites.
Siskind, V., 1972. Second moments of inverse wishart-matrix elements. Biometrika 59, 690–691
1972
Earlier work this paper cites.
Joeveer, M., Einasto, J., Jan. 1978. Has the Universe the Cell Structure? In: Longair, M. S., Einasto, J. (Eds.), Large Scale Structures in the Universe. Vol. 79. p. 241
1978
Earlier work this paper cites.
Peebles, P. J. E., 1980. The Large-Scale Structure of the Universe. Princeton University Press
1980
Earlier work this paper cites.
Rosen, D. V., 1988. Moments for matrix normal variables. Statistics 19 (4), 575–583. URL https://doi.org/10.1080/02331888808802132
1988
Earlier work this paper cites.
Kaiser, N., Apr. 1992. Weak gravitational lensing of distant galaxies. ApJ 388, 272–286
1992
Earlier work this paper cites.
Bunn, E. F., Jan. 1995. Statistical Analysis of Cosmic Microwave Background Anisotropy. Ph.D. Thesis
1995
Earlier work this paper cites.
Tavaré, S., Balding, D. J., Griffiths, R., Donnelly, P., 1997. Inferring coalescence times from DNA sequence data. Genetics 145, 505 – 518
1997
Earlier work this paper cites.
Tegmark, M., Taylor, A., Heavens, A., 1997. Karhunen-Loève Eigenvalue Problems in Cosmology: How Should We Tackle Large Data Sets? ApJ 480, 22
1997
Earlier work this paper cites.
Eisenstein, D. J., Hu, W., Mar. 1998. Baryonic Features in the Matter Transfer Function. ApJ 496, 605
1998
Earlier work this paper cites.
Kaiser, N., May 1998. Weak Lensing and Cosmology. ApJ 498, 26–42
1998
Earlier work this paper cites.
Gupta, A., Nagar, D., 1999. Matrix Variate Distributions. Monographs and Surveys in Pure and Applied Mathematics. Taylor & Francis. URL https://books.google.fr/books?id=PQOYnT7P1loC
1999
Earlier work this paper cites.
Pritchard, J. K., Seielstad, M. T., Perez-Lezaun, A., 1999. Population growth of human Y chromosomes: A study of Y chromosome microsatellites. Molecular Biology and Evolution 16 (12), 1791 – 1798
1999
Earlier work this paper cites.
Heavens, A. F., Jimenez, R., Lahav, O., Oct. 2000. Massive lossless data compression and multiple parameter estimation from galaxy spectra. MNRAS 317, 965–972
2000
Earlier work this paper cites.
Jones, E., Oliphant, T., Peterson, P., et al., 2001–. SciPy: Open source scientific tools for Python. URL http://www.scipy.org/
2001
Earlier work this paper cites.
Schneider, P., Van Waerbeke, L., Kilbinger, M., Mellier, Y., 2002. Analysis of two-point statistics of cosmic shear: I. Estimators and covariances. A&A 396, 1–19
2002
Earlier work this paper cites.
Hartlap, J., Simon, P., Schneider, P., Mar. 2007. Why your model parameter confidences might be too optimistic. Unbiased estimation of the inverse covariance matrix. A&A 464, 399–404
2007
Earlier work this paper cites.
Sisson, S. A., Fan, Y., Tanaka, M. M., 2007. Sequential Monte Carlo without likelihoods. Proceedings of the National Academy of Science 104 (6), 1760 – 1765
2007
Earlier work this paper cites.
Beaumont, M. A., Cornuet, J.-M., Marin, J.-M., Robert, C. P., 2009. Adaptive approximate Bayesian computation. Biometrika 96 (4), 983 – 990
2009
Earlier work this paper cites.
Hamimeche, S., Lewis, A., Apr 2009. Properties and use of CMB power spectrum likelihoods. Phys. Rev. D 79, 083012. URL https://link.aps.org/doi/10.1103/PhysRevD.79.083012
2009
Earlier work this paper cites.
Kilbinger, M., Benabed, K., Guy, et al., 2009. Dark-energy constraints and correlations with systematics from CFHTLS weak lensing, SNLS supernovae Ia and WMAP5. A&A 497, 677–688
2009
Earlier work this paper cites.
2009
Cited alongside, same era.
Beaumont, M. A., 2010. Approximate bayesian computation in evolution and ecology. Annual review of ecology, evolution, and systematics 41, 379–406
2010
Cited alongside, same era.
Seabold, S., Perktold, J., 2010. statsmodels: Econometric and statistical modeling with python. In: 9th Python in Science Conference
2010
Cited alongside, same era.
Cook, R., Forzani, L., 01 2011. On the mean and variance of the generalized inverse of a singular wishart matrix. Electronic Journal of Statistics 5
2011
Cited alongside, same era.
Kilbinger, M., Heymans, C., Asgari, M., et al., 2017. Precision calculations of the cosmic shear power spectrum projection. MNRAS 472, 2126–2141
2017
Later among the works it cites.
Kitching, T. D., Alsing, J., Heavens, A. F., Jimenez, R., McEwen, J. D., Verde, L., Aug. 2017. The limits of cosmic shear. MNRAS 469, 2737–2749
2017
Later among the works it cites.
2017
Later among the works it cites.
Sellentin, E., Heavens, A. F., Feb. 2017. Quantifying lost information due to covariance matrix estimation in parameter inference. MNRAS 464 (4), 4658–4665
2017
Later among the works it cites.
Alsing, J., Wandelt, B., May 2018. Generalized massive optimal data compression. MNRAS 476, L60–L64
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2011
Cited alongside, same era.
2011
Cited alongside, same era.
Moral, P. D., Doucet, A., Jasra, A., 2011. An adaptive sequential Monte Carlo method for approximate Bayesian computation. Statistics and Computing 22 (5), 1009–1020
2011
Cited alongside, same era.
Takahashi, R., Sato, M., Nishimichi, T., Taruya, A., Oguri, M., Dec. 2012. Revising the Halofit Model for the Nonlinear Matter Power Spectrum. ApJ 761, 152
2012
Cited alongside, same era.
Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., Greenfield, P., Droettboom, M., Bray, et al., Oct. 2013. Astropy: A community Python package for astronomy. A&A 558, A33
2013
Cited alongside, same era.
2013
Cited alongside, same era.
Percival, W. J., Ross, A. J., Sánchez, A. G., et al., Apr. 2014. The clustering of Galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: including covariance matrix errors. MNRAS 439, 2531–2541
2014
Cited alongside, same era.
Taylor, A., Joachimi, B., Aug. 2014. Estimating cosmological parameter covariance. MNRAS 442, 2728–2738
2014
Cited alongside, same era.
2018
Later among the works it cites.
Barreira, A., Krause, E., Schmidt, F., Jun 2018b. Complete super-sample lensing covariance in the response approach. JCAP 2018 (6), 015
2018
Later among the works it cites.
Charnock, T., Lavaux, G., Wandelt, B. D., Apr. 2018. Automatic physical inference with information maximizing neural networks. Phys. Rev. D97 (8), 083004
2018
Later among the works it cites.
Friedrich, O., Eifler, T., Jan. 2018. Precision matrix expansion - efficient use of numerical simulations in estimating errors on cosmological parameters. MNRAS 473 (3), 4150–4163
2018
Later among the works it cites.
Harnois-Déraps, J., Amon, A., Choi, A., et al., Nov. 2018. Cosmological simulations for combined-probe analyses: covariance and neighbour-exclusion bias. MNRAS 481 (1), 1337–1367
2018
Later among the works it cites.
Price-Whelan, A. M., Sipőcz, B. M., Günther, H. M., et al., Sep. 2018. The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package. AJ 156, 123
2018
Later among the works it cites.
Chisari, N. E., Mead, A. J., Joudaki, S., et al., Jun 2019. Modelling baryonic feedback for survey cosmology. The Open Journal of Astrophysics 2 (1), 4
2019
Later among the works it cites.
Hahn, C., Beutler, F., Sinha, M., Berlind, A., Ho, S., Hogg, D. W., May 2019. Likelihood non-Gaussianity in large-scale structure analyses. MNRAS 485 (2), 2956–2969
2019
Later among the works it cites.
Hall, A., Taylor, A., Feb. 2019. A Bayesian method for combining theoretical and simulated covariance matrices for large-scale structure surveys. MNRAS 483 (1), 189–207
2019
Later among the works it cites.
Sellentin, E., Starck, J.-L., Aug 2019. Debiasing inference with approximate covariance matrices and other unidentified biases. JCAP2019 (8), 021
2019
Later among the works it cites.
Dalmasso, N., Pospisil, T., Lee, A. B., et al., Jan. 2020. Conditional density estimation tools in python and R with applications to photometric redshifts and likelihood-free cosmological inference. Astronomy and Computing 30, 100362
2020
Later among the works it cites.
Euclid Collaboration, Blanchard, A., et al., Oct. 2020. Euclid preparation. VII. Forecast validation for Euclid cosmological probes. A&A 642, A191
2020
Later among the works it cites.
Kacprzak, T., Herbel, J., Nicola, A., et al., Apr. 2020. Monte Carlo Control Loops for cosmic shear cosmology with DES Year 1. Phys. Rev. D101 (8), 082003
2020
Later among the works it cites.
Lin, C.-H., Harnois-Déraps, J., Eifler, T., Pospisil, T., Mandelbaum, R., Lee, A. B., Singh, S., LSST Dark Energy Science Collaboration, Dec. 2020. Non-Gaussianity in the weak lensing correlation function likelihood - implications for cosmological parameter biases. MNRAS 499 (2), 2977–2993
2020
Later among the works it cites.
Fortuna, M. C., Hoekstra, H., Joachimi, B., et al., Feb. 2021. The halo model as a versatile tool to predict intrinsic alignments. MNRAS 501 (2), 2983–3002
2021
Closest in time.
Jeffrey, N., Alsing, J., Lanusse, F., Feb. 2021. Likelihood-free inference with neural compression of DES SV weak lensing map statistics. MNRAS 501 (1), 954–969
2021
Closest in time.
Riddell, A., Hartikainen, A., Carter, M., Mar. 2021. pystan (3.0.0). PyPI
2021
Closest in time.
2021
Closest in time.
Beaumont, M. A., Zhang, W., Balding, D. J., 2002. Approximate Bayesian computation in population genetics. Genetics 162, 2025 – 2035
2035
Closest in time.
Joudaki, S., Blake, C., Heymans, C., et al., Feb. 2017. CFHTLenS revisited: assessing concordance with Planck including astrophysical systematics. MNRAS 465 (2), 2033–2052
2052
Closest in time.