@ARTICLE{tension,
       author = {{Handley}, Will and {Lemos}, Pablo},
        title = "{Quantifying tension: interpreting the DES evidence ratio}",
      journal = {arXiv e-prints},
     keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = "2019",
        month = "Feb",
          eid = {arXiv:1902.04029},
        pages = {arXiv:1902.04029},
archivePrefix = {arXiv},
       eprint = {1902.04029},
 primaryClass = {astro-ph.CO},
       adsurl = {https://ui.adsabs.harvard.edu/\#abs/2019arXiv190204029H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{dimensionality,
       author = {{Handley}, Will and {Lemos}, Pablo},
        title = "{Quantifying dimensionality: Bayesian cosmological model complexities}",
      journal = {arXiv e-prints},
     keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = "2019",
        month = "Mar",
          eid = {arXiv:1903.06682},
        pages = {arXiv:1903.06682},
archivePrefix = {arXiv},
       eprint = {1903.06682},
 primaryClass = {astro-ph.CO},
       adsurl = {https://ui.adsabs.harvard.edu/\#abs/2019arXiv190306682H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{skilling,
author = "Skilling, John",
doi = "10.1214/06-BA127",
journal = "Bayesian Analysis.",
month = "12",
number = "4",
pages = "833--859",
publisher = "International Society for Bayesian Analysis",
title = "Nested sampling for general Bayesian computation",
url = "https://doi.org/10.1214/06-BA127",
volume = "1",
year = "2006"
}

@ARTICLE{trotta,
   author = {{Trotta}, R.},
    title = "{{Bayes} in the sky: Bayesian inference and model selection in cosmology}",
  journal = {Contemporary Physics},
archivePrefix = "arXiv",
   eprint = {0803.4089},
     year = 2008,
    month = mar,
   volume = 49,
    pages = {71-104},
      doi = {10.1080/00107510802066753},
   adsurl = {http://adsabs.harvard.edu/abs/2008ConPh..49...71T},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}


@ARTICLE{multinest,
   author = {{Feroz}, F. and {Hobson}, M.~P. and {Bridges}, M.},
    title = "{MULTINEST: an efficient and robust Bayesian inference tool for cosmology and particle physics}",
  journal = {Monthly Notices of the Royal Astronomical Society},
archivePrefix = "arXiv",
   eprint = {0809.3437},
 keywords = {methods: data analysis , methods: statistical},
     year = 2009,
    month = oct,
   volume = 398,
    pages = {1601-1614},
      doi = {10.1111/j.1365-2966.2009.14548.x},
   adsurl = {http://adsabs.harvard.edu/abs/2009MNRAS.398.1601F},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{polychord0,
   author = {{Handley}, W.~J. and {Hobson}, M.~P. and {Lasenby}, A.~N.},
    title = "{POLYCHORD: nested sampling for cosmology}",
  journal = {Monthly Notices of the Royal Astronomical Society},
archivePrefix = "arXiv",
   eprint = {1502.01856},
 keywords = {methods: data analysis, methods: statistical},
     year = 2015,
    month = jun,
   volume = 450,
    pages = {L61-L65},
      doi = {10.1093/mnrasl/slv047},
   adsurl = {http://adsabs.harvard.edu/abs/2015MNRAS.450L..61H},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}


@ARTICLE{polychord1,
   author = {{Handley}, W.~J. and {Hobson}, M.~P. and {Lasenby}, A.~N.},
    title = "{POLYCHORD: next-generation nested sampling}",
  journal = {Monthly Notices of the Royal Astronomical Society},
archivePrefix = "arXiv",
   eprint = {1506.00171},
 primaryClass = "astro-ph.IM",
 keywords = {methods: data analysis, methods: statistical},
     year = 2015,
    month = nov,
   volume = 453,
    pages = {4384-4398},
      doi = {10.1093/mnras/stv1911},
   adsurl = {http://adsabs.harvard.edu/abs/2015MNRAS.453.4384H},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{mcmc,
 ISSN = {00063444},
 URL = {http://www.jstor.org/stable/2334940},
 author = {W. K. Hastings},
 journal = {Biometrika},
 number = {1},
 pages = {97--109},
 publisher = {[Oxford University Press, Biometrika Trust]},
 title = {Monte {Carlo} Sampling Methods Using {Markov} Chains and Their Applications},
 doi = {10.2307/2334940},
 volume = {57},
 year = {1970}
}

@article{dnest,
   author = {Brendon Brewer and Daniel Foreman-Mackey},
   title = {DNest4: Diffusive Nested Sampling in C++ and Python},
   journal = {Journal of Statistical Software, Articles},
   volume = {86},
   number = {7},
   year = {2018},
   keywords = {Bayesian inference; Markov chain Monte Carlo; Metropolis algorithm; bayesian computation; nested sampling; C++11; Python},
   abstract = {In probabilistic (Bayesian) inferences, we typically want to compute properties of the posterior distribution, describing knowledge of unknown quantities in the context of a particular dataset and the assumed prior information. The marginal likelihood, also known as the \"evidence\", is a key quantity in Bayesian model selection. The diffusive nested sampling algorithm, a variant of nested sampling, is a powerful tool for generating posterior samples and estimating marginal likelihoods. It is effective at solving complex problems including many where the posterior distribution is multimodal or has strong dependencies between variables. DNest4 is an open source (MIT licensed), multi-threaded implementation of this algorithm in C++11, along with associated utilities including: (i) 'RJObject', a class template for finite mixture models; and (ii) a Python package allowing basic use without C++ coding. In this paper we demonstrate DNest4 usage through examples including simple Bayesian data analysis, finite mixture models, and approximate Bayesian computation.},
   issn = {1548-7660},
   pages = {1--33},
   doi = {10.18637/jss.v086.i07},
   url = {https://www.jstatsoft.org/v086/i07}
}


@Article{gambit,
author="{The GAMBIT Scanner Workgroup: }",
title="Comparison of statistical sampling methods with ScannerBit, the GAMBIT scanning module",
journal="The European Physical Journal C",
year="2017",
month="Nov",
day="13",
volume="77",
number="11",
pages="761",
issn="1434-6052",
doi="10.1140/epjc/s10052-017-5274-y",
url="https://doi.org/10.1140/epjc/s10052-017-5274-y"
}



@ARTICLE{dynesty,
       author = {{Speagle}, Joshua S},
        title = "{dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences}",
      journal = {arXiv e-prints},
     keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Statistics - Computation},
         year = "2019",
        month = "Apr",
          eid = {arXiv:1904.02180},
        pages = {arXiv:1904.02180},
archivePrefix = {arXiv},
       eprint = {1904.02180},
 primaryClass = {astro-ph.IM},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv190402180S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}


@article{dypolychord,
title={{dyPolyChord}: dynamic nested sampling with {PolyChord}},
author={Higson, Edward},
year={2018},
journal={Journal of Open Source Software},
number={29},
pages={916},
volume={3},
doi={10.21105/joss.00965},
url={http://joss.theoj.org/papers/10.21105/joss.00965}
}





@ARTICLE{Higson2017,
    author = {{Higson}, E. and {Handley}, W. and {Hobson}, M. and {Lasenby}, A.
    },
    title = "{Sampling Errors in Nested Sampling Parameter Estimation}",
    journal = {ArXiv e-prints},
    archivePrefix = "arXiv",
    eprint = {1703.09701},
    primaryClass = "stat.ME",
    keywords = {Statistics - Methodology, Astrophysics - Instrumentation and Methods for Astrophysics, Statistics - Applications},
    year = 2017,
    month = mar,
    adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170309701H},
    adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{Higson2018,
    author = {{Higson}, E. and {Handley}, W. and {Hobson}, M. and {Lasenby}, A.
    },
    title = "{Diagnostic Tests for Nested Sampling Calculations}",
    journal = {ArXiv e-prints},
    archivePrefix = "arXiv",
    eprint = {1804.06406},
    primaryClass = "stat.CO",
    keywords = {Statistics - Computation, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Data Analysis, Statistics and Probability},
    year = 2018,
    month = apr,
    adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180406406H},
    adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}


@article{pygtc,
    doi = {10.21105/joss.00046},
    url = {http://dx.doi.org/10.21105/joss.00046},
    year  = {2016},
    month = {oct},
    publisher = {The Open Journal},
    volume = {1},
    number = {6},
    author = {Sebastian Bocquet and Faustin W. Carter},
    title = {pygtc: beautiful parameter covariance plots (aka. Giant Triangle Confusograms)},
    journal = {The Journal of Open Source Software}
}

@Misc{scipy,
    author =    {{Jones}, Eric  and {Oliphant}, Travis and {Peterson}, Pearu},
    title =     {{SciPy}: Open source scientific tools for {Python}},
    year =      {2001},
    url = "http://www.scipy.org/",
    note = {[Online; accessed <today>]}
}

@Misc{getdist,
    author =    {Lewis, Anthony},
    title =     {Getdist Github Repository},
    year =      {2015},
    url = "https://github.com/cmbant/getdist",
    note = {[Online; accessed <today>]}
}

@article{corner,
    Author = {Daniel Foreman-Mackey},
    Doi = {10.21105/joss.00024},
    Title = {corner.py: Scatterplot matrices in Python},
    Journal = {The Journal of Open Source Software},
    Year = 2016,
    Volume = 24,
    Url = {http://dx.doi.org/10.5281/zenodo.45906}
}

@book{mackay,
    author = {MacKay, David J. C.},
    title = {Information Theory, Inference \& Learning Algorithms},
    year = {2002},
    isbn = {0521642981},
    publisher = {Cambridge University Press},
    address = {New York, NY, USA},
} 

@article{KL,
    author = "Kullback, S. and Leibler, R. A.",
    doi = "10.1214/aoms/1177729694",
    fjournal = "The Annals of Mathematical Statistics",
    journal = "Ann. Math. Statist.",
    month = "03",
    number = "1",
    pages = "79--86",
    publisher = "The Institute of Mathematical Statistics",
    title = "On Information and Sufficiency",
    url = "https://doi.org/10.1214/aoms/1177729694",
    volume = "22",
    year = "1951"
}

@ARTICLE{cosmosis,
       author = {{Zuntz}, J. and {Paterno}, M. and {Jennings}, E. and {Rudd}, D. and
         {Manzotti}, A. and {Dodelson}, S. and {Bridle}, S. and {Sehrish}, S. and
         {Kowalkowski}, J.},
        title = "{CosmoSIS: Modular cosmological parameter estimation}",
      journal = {Astronomy and Computing},
     keywords = {Cosmology:miscellaneous, Methods:data analysis, Methods:statistical, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = "2015",
        month = "Sep",
       volume = {12},
        pages = {45-59},
          doi = {10.1016/j.ascom.2015.05.005},
archivePrefix = {arXiv},
       eprint = {1409.3409},
 primaryClass = {astro-ph.CO},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2015A&C....12...45Z},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{montepython,
title = "MontePython 3: Boosted {MCMC} sampler and other features",
journal = "Physics of the Dark Universe",
volume = "24",
pages = "100260",
year = "2019",
issn = "2212-6864",
doi = "10.1016/j.dark.2018.100260",
url = "http://www.sciencedirect.com/science/article/pii/S2212686418302309",
author = "Thejs Brinckmann and Julien Lesgourgues",
keywords = "Cosmology, Parameter inference, Numerical tools",
abstract = "MontePython is a parameter inference package for cosmology. We present the latest development of the code over the past couple of years. We explain, in particular, two new ingredients both contributing to improve the performance of Metropolis–Hastings sampling: an adaptation algorithm for the jumping factor, and a calculation of the inverse Fisher matrix, which can be used as a proposal density. We present several examples to show that these features speed up convergence and can save many hundreds of CPU-hours in the case of difficult runs, with a poor prior knowledge of the covariance matrix. We also summarize all the functionalities of MontePython in the current release, including new likelihoods and plotting options."
}

@article{cosmomc,
      author         = "Lewis, Antony and Bridle, Sarah",
      title          = "{Cosmological parameters from CMB and other data: A Monte
                        Carlo approach}",
      journal        = "Phys. Rev.",
      volume         = "D66",
      year           = "2002",
      pages          = "103511",
      doi            = "10.1103/PhysRevD.66.103511",
      eprint         = "astro-ph/0205436",
      archivePrefix  = "arXiv",
      primaryClass   = "astro-ph",
      SLACcitation   = "%%CITATION = ASTRO-PH/0205436;%%"
}


@article{fastkde,
title = "A fast and objective multidimensional kernel density estimation method: fastKDE",
journal = {Computational Statistics \& Data Analysis},
volume = "101",
pages = "148 - 160",
year = "2016",
issn = "0167-9473",
doi = "10.1016/j.csda.2016.02.014",
url = "http://www.sciencedirect.com/science/article/pii/S0167947316300408",
author = "Travis A. O’Brien and Karthik Kashinath and Nicholas R. Cavanaugh and William D. Collins and John P. O’Brien",
keywords = "Empirical characteristic function, ECF, Kernel density estimation, Histogram, Nonuniform FFT, NuFFT, Multidimensional, KDE",
}

@INPROCEEDINGS{binder,
  author = {{Jupyter et al.}},
  title = "{Binder 2.0 - Reproducible, Interactive, Sharable Environments for Science at Scale.}",
  year = "2018",
  doi = {10.25080/Majora-4af1f417-011},
  series = {Proceedings of the 17th Python in Science Conference.},
}

