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},
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volume = {102},
number = {1},
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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},
}

@article{gelman_2017,
	title = {The {Prior} {Can} {Often} {Only} {Be} {Understood} in the {Context} of the {Likelihood}},
	volume = {19},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {1099-4300},
	url = {https://www.mdpi.com/1099-4300/19/10/555},
	doi = {10.3390/e19100555},
	language = {en},
	number = {10},
	urldate = {2024-12-06},
	journal = {Entropy},
	author = {Gelman, Andrew and Simpson, Daniel and Betancourt, Michael},
	month = oct,
	year = {2017},
	note = {Number: 10
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {Bayesian inference, default priors, prior distribution},
	pages = {555},
}

@article{mikkola_2024,
author = {Petrus Mikkola and Osvaldo A. Martin and Suyog Chandramouli and Marcelo Hartmann and Oriol Abril Pla and Owen Thomas and Henri Pesonen and Jukka Corander and Aki Vehtari and Samuel Kaski and Paul-Christian B{\"u}rkner and Arto Klami},
title = {{Prior Knowledge Elicitation: The Past, Present, and Future}},
volume = {19},
journal = {Bayesian Analysis},
number = {4},
publisher = {International Society for Bayesian Analysis},
pages = {1129 -- 1161},
keywords = {Bayesian workflow, domain knowledge, informative prior, prior distribution, prior elicitation},
year = {2024},
doi = {10.1214/23-BA1381},
URL = {https://doi.org/10.1214/23-BA1381}
}

@book{jaynes_2003,
	address = {Cambridge, UK ; New York, NY},
	title = {Probability {Theory}: {The} {Logic} of {Science}},
	isbn = {978-0-521-59271-0},
	shorttitle = {Probability {Theory}},
	abstract = {Going beyond the conventional mathematics of probability theory, this study views the subject in a wider context. It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis. Aimed at readers already familiar with applied mathematics at an advanced undergraduate level or higher, it is of interest to scientists concerned with inference from incomplete information.},
	publisher = {Cambridge University Press},
	author = {Jaynes, E. T.},
	editor = {Bretthorst, G. Larry},
	month = jun,
	year = {2003},
}

@article{icazatti_2023,
author = {Icazatti, Alejandro and Abril-Pla, Oriol and Klami, Arto and Martin, Osvaldo A},
doi = {10.21105/joss.05499},
journal = {Journal of Open Source Software},
month = sep,
number = {89},
pages = {5499},
title = {{PreliZ: A tool-box for prior elicitation}},
url = {https://joss.theoj.org/papers/10.21105/joss.05499},
volume = {8},
year = {2023}
}

@misc{gelman_2020,
      title={Bayesian Workflow}, 
      author={Andrew Gelman and Aki Vehtari and Daniel Simpson and Charles C. Margossian and Bob Carpenter and Yuling Yao and Lauren Kennedy and Jonah Gabry and Paul-Christian Bürkner and Martin Modrák},
      year={2020},
      eprint={2011.01808},
      archivePrefix={arXiv},
      primaryClass={stat.ME},
      url={https://arxiv.org/abs/2011.01808}, 
}

@article{morris_2014,
title = {A web-based tool for eliciting probability distributions from experts},
journal = {Environmental Modelling & Software},
volume = {52},
pages = {1-4},
year = {2014},
issn = {1364-8152},
doi = {https://doi.org/10.1016/j.envsoft.2013.10.010},
url = {https://www.sciencedirect.com/science/article/pii/S1364815213002533},
author = {David E. Morris and Jeremy E. Oakley and John A. Crowe},
keywords = {Bayesian prior distribution, Expert judgement, Subjective probability, Web-based elicitation}
}

@book{martin_2021,
	address = {Boca Raton London New York},
	edition = {1st edition},
	title = {Bayesian {Modeling} and {Computation} in {Python}},
	isbn = {978-0-367-89436-8},
	language = {English},
	publisher = {Chapman and Hall/CRC},
	author = {Martin, Osvaldo A. and Kumar, Ravin and Lao, Junpeng},
	month = dec,
	year = {2021},
}

@book{martin_2024,
	title = {Bayesian {Analysis} with {Python}: {A} {Practical} {Guide} to probabilistic modeling, 3rd {Edition}},
	isbn = {978-1-80512-716-1},
	shorttitle = {Bayesian {Analysis} with {Python}},
	language = {English},
	publisher = {Packt Publishing},
	author = {Martin, Osvaldo A},
	month = feb,
	year = {2024},
}
           
@article{chipman_2010,
	title = {{BART}: {Bayesian} additive regression trees},
	volume = {4},
	issn = {1932-6157},
	shorttitle = {{BART}},
	url = {http://projecteuclid.org/euclid.aoas/1273584455},
	doi = {10.1214/09-AOAS285},
	language = {en},
	number = {1},
	urldate = {2019-02-21},
	journal = {The Annals of Applied Statistics},
	author = {Chipman, Hugh A. and George, Edward I. and McCulloch, Robert E.},
	month = mar,
	year = {2010},
	pages = {266--298},
}

@article{vehtari_2017,
  author = {Vehtari, Aki and Gelman, Andrew and Gabry, Jonah},
  title = {Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC},
  journal = {Statistics and Computing},
  year = {2017},
  volume = {27},
  number = {5},
  pages = {1413--1432},
  doi = {10.1007/s11222-016-9696-4},
  url = {https://doi.org/10.1007/s11222-016-9696-4},
}

@article{yao_2018,
author = {Yuling Yao and Aki Vehtari and Daniel Simpson and Andrew Gelman},
title = {{Using Stacking to Average Bayesian Predictive Distributions (with Discussion)}},
volume = {13},
journal = {Bayesian Analysis},
number = {3},
publisher = {International Society for Bayesian Analysis},
pages = {917 -- 1007},
keywords = {Bayesian model averaging, model combination, predictive distribution, proper scoring rule, stacking, Stan},
year = {2018},
doi = {10.1214/17-BA1091},
URL = {https://doi.org/10.1214/17-BA1091},
}

@article{watanabe_2013,
	title = {A {Widely} {Applicable} {Bayesian} {Information} {Criterion}},
	volume = {14},
	journal = {Journal of Machine Learning Research},
	author = {Watanabe, Sumio},
	month = mar,
	year = {2013},
	pages = {867--897},
}

@article{akaike_1974,
  author={Akaike, H.},
  journal={IEEE Transactions on Automatic Control}, 
  title={A new look at the statistical model identification}, 
  year={1974},
  volume={19},
  number={6},
  pages={716-723},
  doi={10.1109/TAC.1974.1100705},
  }

@article{vehtari_2021,
author = {Aki Vehtari and Andrew Gelman and Daniel Simpson and Bob Carpenter and Paul-Christian B{\"u}rkner},
title = {{Rank-Normalization, Folding, and Localization: An Improved $\widehat{R}$ for Assessing Convergence of MCMC (with Discussion)}},
volume = {16},
journal = {Bayesian Analysis},
number = {2},
publisher = {International Society for Bayesian Analysis},
pages = {667 -- 718},
year = {2021},
doi = {10.1214/20-BA1221},
URL = {https://doi.org/10.1214/20-BA1221}
}


@article{piironen_2020,
author = {Juho Piironen and Markus Paasiniemi and Aki Vehtari},
title = {{Projective inference in high-dimensional problems: Prediction and feature selection}},
volume = {14},
journal = {Electronic Journal of Statistics},
number = {1},
publisher = {Institute of Mathematical Statistics and Bernoulli Society},
pages = {2155 -- 2197},
keywords = {Feature selection, Post-selection inference, prediction, projection, Sparsity},
year = {2020},
doi = {10.1214/20-EJS1711},
URL = {https://doi.org/10.1214/20-EJS1711}
}

@misc{quiroga_2022,
  doi = {10.48550/ARXIV.2206.03619},
  url = {https://arxiv.org/abs/2206.03619},
  author = {Quiroga, Miriana and Garay, Pablo G and Alonso, Juan M. and Loyola, Juan Martin and Martin, Osvaldo A},
  keywords = {Computation (stat.CO), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Bayesian additive regression trees for probabilistic programming},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution Share Alike 4.0 International}
}

@misc{mclatchie_2023,
    title={Robust and efficient projection predictive inference},
    author={Yann McLatchie and Sölvi Rögnvaldsson and Frank Weber and Aki Vehtari},
    year={2023},
    eprint={2306.15581},
    archivePrefix={arXiv},
    primaryClass={stat.ME}
}