references.bib

@article{William1984,
author = { William S.   Cleveland  and  Robert   McGill },
title = {Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods},
journal = {Journal of the American Statistical Association},
volume = {79},
number = {387},
pages = {531-554},
year  = {1984},
publisher = {Taylor & Francis},
doi = {10.1080/01621459.1984.10478080},
URL = { https://www.tandfonline.com/doi/abs/10.1080/01621459.1984.10478080},
eprint = { https://www.tandfonline.com/doi/pdf/10.1080/01621459.1984.10478080},
}

@inproceedings{Heer2010,
	address = {New York, NY, USA},
	series = {{CHI} '10},
	title = {Crowdsourcing graphical perception: using mechanical turk to assess visualization design},
	isbn = {978-1-60558-929-9},
	shorttitle = {Crowdsourcing graphical perception},
	url = {https://doi.org/10.1145/1753326.1753357},
	doi = {10.1145/1753326.1753357},
	urldate = {2023-09-28},
	booktitle = {Proceedings of the {SIGCHI} {Conference} on {Human} {Factors} in {Computing} {Systems}},
	publisher = {Association for Computing Machinery},
	author = {Heer, Jeffrey and Bostock, Michael},
	month = apr,
	year = {2010},
	keywords = {crowdsourcing, evaluation, experimentation, graphical perception, information visualization, mechanical turk, user study},
	pages = {203--212},
}

@incollection{diaconis2011,
	title = {Theories of {Data} {Analysis}: {From} {Magical} {Thinking} {Through} {Classical} {Statistics}},
	copyright = {Copyright © 1985, 2006 John Wiley \& Sons, Inc. All rights reserved.},
	isbn = {978-1-118-15070-2},
	shorttitle = {Theories of {Data} {Analysis}},
	abstract = {This chapter contains sections titled: Intuitive Statistics— Some Inferential Problems Multiplicity— A Pervasive Problem Some Remedies Theories for Data Analysis Uses for Mathematics In Defense of Controlled Magical Thinking},
	booktitle = {Exploring {Data} {Tables}, {Trends}, and {Shapes}},
	publisher = {John Wiley \& Sons, Ltd},
	author = {Diaconis, Persi},
	year = {2011},
	doi = {10.1002/9781118150702.ch1},
	keywords = {controlled magical thinking, data analysis, data structure, intuitive statistics, multiplicity},
	pages = {1--36},
}

@article{ghahramani2015,
	title = {Probabilistic {Machine} {Learning} and {Artificial} {Intelligence}},
	volume = {521},
	copyright = {© 2015 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	issn = {0028-0836},
	doi = {10.1038/nature14541},
	abstract = {How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.},
	number = {7553},
	journal = {Nature},
	author = {Ghahramani, Zoubin},
	month = may,
	year = {2015},
	keywords = {Computer science, Mathematics and computing, neuroscience},
	pages = {452--459},
}

@book{bessiere2013,
	address = {Boca Raton},
	edition = {1 edition},
	title = {Bayesian {Programming}},
	isbn = {978-1-4398-8032-6},
	publisher = {Chapman and Hall/CRC},
    url = {https://www.crcpress.com/Bayesian-Programming/Bessiere-Mazer-Ahuactzin-Mekhnacha/p/book/9781439880326},
	author = {Bessiere, Pierre and Mazer, Emmanuel and Ahuactzin, Juan Manuel and Mekhnacha, Kamel},
	month = dec,
	year = {2013},
}

@book{daniel2015,
	title = {Probabilistic {Programming}},
	author = {{Daniel Roy}},
    url = {http://probabilistic-programming.org},
	year = {2015},
}


@article{xarray_2017,
	title = {Xarray: {N}-{D} {Labeled} {Arrays} and {Datasets} in {Python}},
	volume = {5},
	issn = {2049-9647},
	shorttitle = {Xarray},
	doi = {10.5334/jors.148},
	number = {1},
	journal = {Journal of Open Research Software},
	author = {Hoyer, Stephan and Hamman, Joe},
	month = apr,
	year = {2017},
	keywords = {data analysis, data, data handling, multidimensional, netCDF, pandas, Python},
}

@article{Kleiber_2016,
   title={Visualizing Count Data Regressions Using Rootograms},
   volume={70},
   ISSN={1537-2731},
   url={http://dx.doi.org/10.1080/00031305.2016.1173590},
   DOI={10.1080/00031305.2016.1173590},
   number={3},
   journal={The American Statistician},
   publisher={Informa UK Limited},
   author={Kleiber, Christian and Zeileis, Achim},
   year={2016},
   month=jul, pages={296–303} }

@article{Brockmann_1996,
author = {Brockmann, H. Jane},
title = {Satellite Male Groups in Horseshoe Crabs, Limulus polyphemus},
journal = {Ethology},
volume = {102},
number = {1},
pages = {1-21},
doi = {https://doi.org/10.1111/j.1439-0310.1996.tb01099.x},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1439-0310.1996.tb01099.x},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1439-0310.1996.tb01099.x},
year = {1996}
}

@book{tukey_1977,
	edition = {1 edition},
	title = {Exploratory {Data} {Analysis}},
	isbn = {978-0-201-07616-5},
	publisher = {Pearson},
	author = {Tukey, John W.},
	year = {1977},
}

@article{Greenhill_2011,
author = {Greenhill, Brian and Ward, Michael D. and Sacks, Audrey},
title = {The Separation Plot: A New Visual Method for Evaluating the Fit of Binary Models},
journal = {American Journal of Political Science},
volume = {55},
number = {4},
pages = {991-1002},
doi = {https://doi.org/10.1111/j.1540-5907.2011.00525.x},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-5907.2011.00525.x},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1540-5907.2011.00525.x},
year = {2011}
}

@article{kallioinen_2023,
	title = {Detecting and diagnosing prior and likelihood sensitivity with power-scaling},
	volume = {34},
	issn = {1573-1375},
	url = {https://doi.org/10.1007/s11222-023-10366-5},
	doi = {10.1007/s11222-023-10366-5},
	language = {en},
	number = {1},
	urldate = {2024-09-25},
	journal = {Statistics and Computing},
	author = {Kallioinen, Noa and Paananen, Topi and Bürkner, Paul-Christian and Vehtari, Aki},
	month = dec,
	year = {2023},
	keywords = {Artificial Intelligence, Bayesian, diagnostic, likelihood, prior, sensitivity},
	pages = {57},
}

@article{sailynoja_2022,
	title = {Graphical test for discrete uniformity and its applications in goodness-of-fit evaluation and multiple sample comparison},
	volume = {32},
	issn = {1573-1375},
	url = {https://doi.org/10.1007/s11222-022-10090-6},
	doi = {10.1007/s11222-022-10090-6},
	language = {en},
	number = {2},
	urldate = {2024-10-07},
	journal = {Statistics and Computing},
	author = {Säilynoja, Teemu and Bürkner, Paul-Christian and Vehtari, Aki},
	month = mar,
	year = {2022},
	keywords = {Artificial Intelligence, ECDF, MCMC convergence diagnostic, PIT, Simulation-based calibration, Uniformity test},
	pages = {32},
}

@misc{talts_2020,
      title={Validating Bayesian Inference Algorithms with Simulation-Based Calibration}, 
      author={Sean Talts and Michael Betancourt and Daniel Simpson and Aki Vehtari and Andrew Gelman},
      year={2020},
      eprint={1804.06788},
      archivePrefix={arXiv},
      primaryClass={stat.ME},
      url={https://arxiv.org/abs/1804.06788}, 
}

@article{link_2011,
author = {Link, William A. and Eaton, Mitchell J.},
title = {On thinning of chains in MCMC},
journal = {Methods in Ecology and Evolution},
volume = {3},
number = {1},
pages = {112-115},
keywords = {Markov chain Monte Carlo, thinning, WinBUGS},
doi = {https://doi.org/10.1111/j.2041-210X.2011.00131.x},
url = {https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/j.2041-210X.2011.00131.x},
eprint = {https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/j.2041-210X.2011.00131.x},
year = {2012},
}

@article{maceachern_1994,
	title = {Subsampling the {Gibbs} {Sampler}},
	volume = {48},
	issn = {0003-1305},
	url = {https://www.jstor.org/stable/2684714},
	doi = {10.2307/2684714},
	number = {3},
	urldate = {2024-10-07},
	journal = {The American Statistician},
	author = {MacEachern, Steven N. and Berliner, L. Mark},
	year = {1994},
	note = {Publisher: [American Statistical Association, Taylor \& Francis, Ltd.]},
	pages = {188--190},
}