@Article{         numpy,
 title         = {Array2D programming with {NumPy}},
 author        = {Charles R. Harris and K. Jarrod Millman and St{'{e}}fan J.
                 van der Walt and Ralf Gommers and Pauli Virtanen and David
                 Cournapeau and Eric Wieser and Julian Taylor and Sebastian
                 Berg and Nathaniel J. Smith and Robert Kern and Matti Picus
                 and Stephan Hoyer and Marten H. van Kerkwijk and Matthew
                 Brett and Allan Haldane and Jaime Fern{'{a}}ndez del
                 R{'{\i}}o and Mark Wiebe and Pearu Peterson and Pierre
                 G{'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and
                 Warren Weckesser and Hameer Abbasi and Christoph Gohlke and
                 Travis E. Oliphant},
 year          = {2020},
 month         = sep,
 journal       = {Nature},
 volume        = {585},
 number        = {7825},
 pages         = {357--362},
 doi           = {10.1038/s41586-020-2649-2},
 publisher     = {Springer Science and Business Media {LLC}},
 url           = {https://doi.org/10.1038/s41586-020-2649-2}
}

@article{corner,
  doi = {10.21105/joss.00024},
  url = {https://doi.org/10.21105/joss.00024},
  year  = {2016},
  month = {jun},
  publisher = {The Open Journal},
  volume = {1},
  number = {2},
  pages = {24},
  author = {Daniel Foreman-Mackey},
  title = {corner.py: Scatterplot matrices in Python},
  journal = {The Journal of Open Source Software}
}
@article{pyswarms,
    author  = {Lester James V. Miranda},
    title   = "{P}y{S}warms, a research-toolkit for {P}article {S}warm {O}ptimization in {P}ython",
    journal = {Journal of Open Source Software},
    year    = {2018},
    volume  = {3},
    issue   = {21},
    doi     = {10.21105/joss.00433},
    url     = {https://doi.org/10.21105/joss.00433}
}

@article{dynesty,
    author = {Speagle, Joshua S},
    title = "{dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences}",
    journal = {Monthly Notices of the Royal Astronomical Society},
    volume = {493},
    number = {3},
    pages = {3132-3158},
    year = {2020},
    month = {02},
    issn = {0035-8711},
    doi = {10.1093/mnras/staa278},
    url = {https://doi.org/10.1093/mnras/staa278},
    eprint = {https://academic.oup.com/mnras/article-pdf/493/3/3132/32890730/staa278.pdf},
}

@ARTICLE{emcee,
       author = {{Foreman-Mackey}, Daniel and {Hogg}, David W. and {Lang}, Dustin and {Goodman}, Jonathan},
        title = "{emcee: The MCMC Hammer}",
      journal = {Publications of the Astronomical Society of the Pacific},
     keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Computational Physics, Statistics - Computation},
         year = 2013,
        month = mar,
       volume = {125},
       number = {925},
        pages = {306},
          doi = {10.1086/670067},
archivePrefix = {arXiv},
       eprint = {1202.3665},
 primaryClass = {astro-ph.IM},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@Article{matplotlib,
  Author    = {Hunter, J. D.},
  Title     = {Matplotlib: A 2D graphics environment},
  Journal   = {Computing in Science \& Engineering},
  Volume    = {9},
  Number    = {3},
  Pages     = {90--95},
  abstract  = {Matplotlib is a 2D graphics package used for Python for
  application development, interactive scripting, and publication-quality
  image generation across user interfaces and operating systems.},
  publisher = {IEEE COMPUTER SOC},
  doi       = {10.1109/MCSE.2007.55},
  year      = 2007
}
@ARTICLE{scipy,
       author = {{Virtanen}, Pauli and {Gommers}, Ralf and {Oliphant},
         Travis E. and {Haberland}, Matt and {Reddy}, Tyler and
         {Cournapeau}, David and {Burovski}, Evgeni and {Peterson}, Pearu
         and {Weckesser}, Warren and {Bright}, Jonathan and {van der Walt},
         St{\'e}fan J.  and {Brett}, Matthew and {Wilson}, Joshua and
         {Jarrod Millman}, K.  and {Mayorov}, Nikolay and {Nelson}, Andrew
         R.~J. and {Jones}, Eric and {Kern}, Robert and {Larson}, Eric and
         {Carey}, CJ and {Polat}, {\.I}lhan and {Feng}, Yu and {Moore},
         Eric W. and {Vand erPlas}, Jake and {Laxalde}, Denis and
         {Perktold}, Josef and {Cimrman}, Robert and {Henriksen}, Ian and
         {Quintero}, E.~A. and {Harris}, Charles R and {Archibald}, Anne M.
         and {Ribeiro}, Ant{\^o}nio H. and {Pedregosa}, Fabian and
         {van Mulbregt}, Paul and {Contributors}, SciPy 1. 0},
        title = "{SciPy 1.0: Fundamental Algorithms for Scientific
                  Computing in Python}",
      journal = {Nature Methods},
      year = "2020",
      volume={17},
      pages={261--272},
      adsurl = {https://rdcu.be/b08Wh},
      doi = {10.1038/s41592-019-0686-2},
}
@article{Haussler2013,
archivePrefix = {arXiv},
arxivId = {1212.3332},
author = {H{\"{a}}u{\ss}ler, Boris and Bamford, Steven P. and Vika, Marina and Rojas, Alex L. and Barden, Marco and Kelvin, Lee S. and Alpaslan, Mehmet and Robotham, Aaron S.G. and Driver, Simon P. and Baldry, I. K. and Brough, Sarah and Hopkins, Andrew M. and Liske, Jochen and Nichol, Robert C. and Popescu, Cristina C. and Tuffs, Richard J.},
doi = {10.1093/mnras/sts633},
eprint = {1212.3332},
issn = {00358711},
journal = {Monthly Notices of the Royal Astronomical Society},
keywords = {galaxies: fundamental parameters,galaxies: structure,Techniques: image processing,methods: data analysis},
month = {mar},
number = {1},
pages = {330--369},
title = {{Megamorph - multiwavelength measurement of galaxy structure: Complete S{\'{e}}rsic profile information from modern surveys}},
volume = {430},
year = {2013}
}
@article{Nightingale2015,
archivePrefix = {arXiv},
arxivId = {1412.7436},
author = {Nightingale, J. W. and Dye, S.},
doi = {10.1093/mnras/stv1455},
eprint = {1412.7436},
issn = {13652966},
journal = {Monthly Notices of the Royal Astronomical Society},
keywords = {galaxies: evolution,galaxies: structure,Methods: observational},
month = {sep},
number = {3},
pages = {2940--2959},
title = {{Adaptive semi-linear inversion of strong gravitational lens imaging}},
volume = {452},
year = {2015}
}
@article{Nightingale2018,
archivePrefix = {arXiv},
arxivId = {1708.07377},
author = {Nightingale, J. W. and Dye, S. and Massey, Richard J.},
doi = {10.1093/mnras/sty1264},
eprint = {1708.07377},
file = {:home/jammy/Documents/Papers{\_}Me/AutoLensChangesMarked.pdf:pdf},
issn = {13652966},
journal = {Monthly Notices of the Royal Astronomical Society},
keywords = {Galaxy: structure,Gravitational lensing,Methods: data analysis},
number = {4},
pages = {4738--4784},
title = {{AutoLens: Automated modeling of a strong lens's light, mass, and source}},
url = {https://academic.oup.com/mnras/article/478/4/4738/5001434},
volume = {478},
year = {2018}
}
@article{Nightingale2019,
archivePrefix = {arXiv},
arxivId = {1901.07801},
author = {Nightingale, J. W. and Massey, Richard J. and Harvey, David R. and Cooper, Andrew P. and Etherington, Amy and Tam, Sut Ieng and Hayes, Richard G.},
doi = {10.1093/mnras/stz2220},
eprint = {1901.07801},
file = {:home/jammy/Documents/Papers{\_}Me/Gal{\_}Structure{\_}Final/GalaxyStructure.pdf:pdf},
issn = {13652966},
journal = {Monthly Notices of the Royal Astronomical Society},
keywords = {galaxies: Evolution,galaxies: Formation,Gravitational lensing: Strong},
number = {2},
pages = {2049--2068},
title = {{Galaxy structure with strong gravitational lensing: Decomposing the internal mass distribution of massive elliptical galaxies}},
url = {http://arxiv.org/abs/1901.07801},
volume = {489},
year = {2019}
}
@article{Salvatier2016,
archivePrefix = {arXiv},
arxivId = {1507.08050},
author = {Salvatier, John and Wiecki, Thomas V. and Fonnesbeck, Christopher},
doi = {10.7717/peerj-cs.55},
eprint = {1507.08050},
file = {:home/jammy/Documents/Papers/PPLs/PyMC3.pdf:pdf},
issn = {23765992},
journal = {PeerJ Computer Science},
keywords = {Bayesian statistic,Markov chain Monte Carlo,Probabilistic Programming,Python,Statistical modeling},
number = {4},
pages = {1--24},
title = {{Probabilistic programming in Python using PyMC3}},
volume = {2016},
year = {2016}
}
@article{Carpenter2017,
author = {Carpenter, Bob and Gelman, Andrew and Hoffman, Matthew D. and Lee, Daniel and Goodrich, Ben and Betancourt, Michael and Brubaker, Marcus A. and Guo, Jiqiang and Li, Peter and Riddell, Allen},
doi = {10.18637/jss.v076.i01},
file = {:home/jammy/Documents/Papers/PPLs/STAN.pdf:pdf},
issn = {15487660},
journal = {Journal of Statistical Software},
keywords = {Algorithmic differentiation,Bayesian inference,Probabilistic program,Stan},
number = {1},
title = {{Stan: A probabilistic programming language}},
volume = {76},
year = {2017}
}
@article{Bingham2019,
archivePrefix = {arXiv},
arxivId = {1810.09538},
author = {Bingham, Eli and Chen, Jonathan P. and Jankowiak, Martin and Obermeyer, F. and Pradhan, Neeraj and Karaletsos, Theofanis and Singh, Rohit and Szerlip, Paul and Horsfall, Paul and Goodman, Noah D.},
eprint = {1810.09538},
file = {:home/jammy/Documents/Papers/PPLs/Pyro.pdf:pdf},
issn = {15337928},
journal = {Journal of Machine Learning Research},
keywords = {Approximate Bayesian inference,Deep learning,Generative models,Graphical models,Probabilistic programming},
number = {Xxxx},
pages = {0--5},
title = {{Pyro: Deep universal probabilistic programming}},
volume = {20},
year = {2019}
}
@ARTICLE{tensorflow,
       author = {{Dillon}, Joshua V. and {Langmore}, Ian and {Tran}, Dustin and
         {Brevdo}, Eugene and {Vasudevan}, Srinivas and {Moore}, Dave and
         {Patton}, Brian and {Alemi}, Alex and {Hoffman}, Matt and
         {Saurous}, Rif A.},
        title = "{TensorFlow Distributions}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Programming Languages, Statistics - Machine Learning},
         year = 2017,
        month = nov,
          eid = {arXiv:1711.10604},
        pages = {arXiv:1711.10604},
archivePrefix = {arXiv},
       eprint = {1711.10604},
 primaryClass = {cs.LG},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2017arXiv171110604D},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{pymultinest,
archivePrefix = {arXiv},
arxivId = {1402.0004},
author = {Buchner, J. and Georgakakis, A. and Nandra, K. and Hsu, L. and Rangel, C. and Brightman, M. and Merloni, A. and Salvato, M. and Donley, J. and Kocevski, D.},
doi = {10.1051/0004-6361/201322971},
eprint = {1402.0004},
file = {:home/jammy/Documents/Papers/Stats/ButchnerPyMultiNest.pdf:pdf},
issn = {14320746},
journal = {Astronomy and Astrophysics},
keywords = {Accretion, accretion disks,galaxies: high-redshift,galaxies: nuclei,Methods: data analysis,Methods: statistical,X-rays: galaxies},
pages = {A125},
title = {{X-ray spectral modelling of the AGN obscuring region in the CDFS: Bayesian model selection and catalogue}},
volume = {564},
year = {2014}
}
@article{multinest,
archivePrefix = {arXiv},
arxivId = {0809.3437},
author = {Feroz, F. and Hobson, M. P. and Bridges, M.},
doi = {10.1111/j.1365-2966.2009.14548.x},
eprint = {0809.3437},
isbn = {0035-8711},
issn = {00358711},
journal = {Monthly Notices of the Royal Astronomical Society},
keywords = {Methods: Data analysis,Methods: Statistical},
number = {4},
pages = {1601--1614},
pmid = {29176},
title = {{MultiNest: An efficient and robust Bayesian inference tool for cosmology and particle physics}},
volume = {398},
year = {2009}
}
@book{python,
 author = {Van Rossum, Guido and Drake, Fred L.},
 title = {Python 3 Reference Manual},
 year = {2009},
 isbn = {1441412697},
 publisher = {CreateSpace},
 address = {Scotts Valley, CA}
}
@article{uravu,
  doi = {10.21105/joss.02214},
  url = {https://doi.org/10.21105/joss.02214},
  year = {2020},
  publisher = {The Open Journal},
  volume = {5},
  number = {50},
  pages = {2214},
  author = {Andrew R. McCluskey and Tim Snow},
  title = {uravu: Making Bayesian modelling easy(er)},
  journal = {Journal of Open Source Software}
}
