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paper.bib
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@Misc{skypy,
author = {A. Amara and et al.},
title = {SkyPy: A package for modelling the Universe.},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
url = {https://github.com/skypyproject/skypy}
}
@article{firesong,
doi = {10.21105/joss.03194},
url = {https://doi.org/10.21105/joss.03194},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {61},
pages = {3194},
author = {Chun Fai Tung and Theo Glauch and Michael Larson and Alex Pizzuto and Rene Reimann and Ignacio Taboada},
title = {FIRESONG: A python package to simulate populations of extragalactic neutrino sources},
journal = {Journal of Open Source Software}
}
@Misc{selection,
author = {W. Farr},
title = {An example of treating selection effects via summing over non-detections in Stan.},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
url = {https://github.com/farr/SelectionExample}
}
@article{Loredo:2019,
year = {2019},
title = {{Multilevel and hierarchical Bayesian modeling of cosmic populations}},
author = {Loredo, Thomas J and Hendry, Martin A},
journal = {arXiv},
eprint = {1911.12337},
url = {arXiv.org},
abstract = {{Demographic studies of cosmic populations must contend with measurement errors and selection effects. We survey some of the key ideas astronomers have developed to deal with these complications, in the context of galaxy surveys and the literature on corrections for Malmquist and Eddington bias. From the perspective of modern statistics, such corrections arise naturally in the context of multilevel models, particularly in Bayesian treatments of such models: hierarchical Bayesian models. We survey some key lessons from hierarchical Bayesian modeling, including shrinkage estimation, which is closely related to traditional corrections devised by astronomers. We describe a framework for hierarchical Bayesian modeling of cosmic populations, tailored to features of astronomical surveys that are not typical of surveys in other disciplines. This thinned latent marked point process framework accounts for the tie between selection (detection) and measurement in astronomical surveys, treating selection and measurement error effects in a self-consistent manner.}},
volume = {astro-ph.IM},
keywords = {},
month = {11},
local-url = {file://localhost/Users/jburgess/Documents/Papers%20Library/Loredo/2019/Loredo_2019_arXiv.pdf}
}
@article{Mortlock:2019,
year = {2019},
title = {{Unbiased Hubble constant estimation from binary neutron star mergers}},
author = {Mortlock, Daniel J. and Feeney, Stephen M. and Peiris, Hiranya V. and Williamson, Andrew R. and Nissanke, Samaya M.},
journal = {Physical Review D},
issn = {2470-0010},
doi = {10.1103/physrevd.100.103523},
eprint = {1811.11723},
abstract = {{Gravitational-wave (GW) observations of binary neutron star (BNS) mergers can be used to measure luminosity distances and hence, when coupled with estimates for the mergers’ host redshifts, infer the Hubble constant H0. These observations are, however, affected by GW measurement noise, uncertainties in host redshifts and peculiar velocities, and are potentially biased by selection effects and the misspecification of the cosmological model or the BNS population. The estimation of H0 from samples of BNS mergers with optical counterparts is tested here by using a phenomenological model for the GW strains that captures both the data-driven event selection and the distance-inclination degeneracy, while being simple enough to facilitate large numbers of simulations. A rigorous Bayesian approach to analyzing the data from such simulated BNS merger samples is shown to yield results that are unbiased, have the appropriate uncertainties, and are robust to model misspecification. Applying such methods to a sample of N≃50 BNS merger events, as LIGO+Virgo could produce in the next ∼5 years, should yield robust and accurate Hubble constant estimates that are precise to a level of ≲2 km s-1 Mpc-1, sufficient to reliably resolve the current tension between local and cosmological measurements of H0.}},
pages = {103523},
number = {10},
volume = {100},
keywords = {},
local-url = {file://localhost/Users/jburgess/Documents/Papers%20Library/Mortlock/2019/Mortlock_2019_Physical%20Review%20D.pdf}
}