references.bib

@article{ando2007bayesian,
  title         = {Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models},
  author        = {Ando, Tomohiro},
  journal       = {Biometrika},
  volume        = {94},
  number        = {2},
  pages         = {443--458},
  year          = {2007},
  publisher     = {Oxford University Press},
  doi           = {10.1093/biomet/asm017}
}
@book{angrist2008harmless,
  title         = {Mostly Harmless Econometrics},
  author        = {Angrist, Joshua and Pischke, J\"{o}rn-Steffen},
  year          = {2008},
  publisher     = {Princeton University Press}
}
@book{aronow2019agnostic,
  title         = {Foundations of Agnostic Statistics},
  author        = {Aronow, Peter and Miller, Benjamin},
  year          = {2019},
  publisher     = {Cambridge University Press}
}
@article{baio2010bayesian,
  title         = {Bayesian hierarchical model for the prediction of football results},
  author        = {Baio, Gianluca and Blangiardo, Marta},
  journal       = {Journal of Applied Statistics},
  volume        = {37},
  number        = {2},
  pages         = {253--264},
  year          = {2010},
  publisher     = {Taylor \& Francis}
}
@article{bali2003gev,
  title         = {The generalized extreme value distribution},
  journal       = {Economics Letters},
  volume        = {79},
  number        = {3},
  pages         = {423--427},
  year          = {2003},
  issn          = {0165-1765},
  doi           = {https://doi.org/10.1016/S0165-1765(03)00035-1},
  url           = {https://www.sciencedirect.com/science/article/pii/S0165176503000351},
  author        = {Turan G. Bali}
}
@article{bauer2005probing,
  title         = {Probing interactions in fixed and multilevel regression: Inferential and graphical techniques},
  author        = {Bauer, Daniel J and Curran, Patrick J},
  journal       = {Multivariate behavioral research},
  volume        = {40},
  number        = {3},
  pages         = {373--400},
  year          = {2005},
  publisher     = {Taylor \& Francis}
}
@book{berry1996statistics,
  title         = {Statistics: a Bayesian perspective},
  author        = {Berry, Donald A},
  year          = {1996},
  publisher     = {Duxbury Press}
}
@article{bertrand2004much,
  title         = {How much should we trust differences-in-differences estimates?},
  author        = {Bertrand, Marianne and Duflo, Esther and Mullainathan, Sendhil},
  journal       = {The Quarterly journal of economics},
  volume        = {119},
  number        = {1},
  pages         = {249--275},
  year          = {2004},
  publisher     = {MIT Press}
}
@article{besag1991bayesian,
  title         = {Bayesian image restoration, with two applications in spatial statistics},
  author        = {Besag, Julian and York, Jeremy and Molli{\'e}, Annie},
  journal       = {Annals of the institute of statistical mathematics},
  volume        = {43},
  number        = {1},
  pages         = {1--20},
  year          = {1991},
  publisher     = {Springer}
}
@article{bonilla2007multioutput,
  title         = {Multi-task Gaussian process prediction},
  author        = {Bonilla, Edwin V and Chai, Kian and Williams, Christopher},
  journal       = {Advances in neural information processing systems},
  volume        = {20},
  year          = {2007},
  url           = {https://papers.nips.cc/paper/2007/hash/66368270ffd51418ec58bd793f2d9b1b-Abstract.html}
}
@book{breen1996regression,
  title         = {Regression models: Censored, sample selected, or truncated data},
  author        = {Breen, Richard and others},
  volume        = {111},
  year          = {1996},
  publisher     = {Sage}
}
@article{burkner2018,
  title         = {Modeling Monotonic Effects of Ordinal Predictors in Bayesian Regression Models},
  author        = {B{\"u}rkner P, \& Charpentier E},
  year          = {2018},
  journal       = {PsyArXiv},
  url           = {https://doi.org/10.31234/osf.io/9qkhj},
  doi           = {doi:10.31234/osf.io/9qkhj}
}
@inproceedings{caprani2009gev,
  title         = "Estimating extreme highway bridge traffic load effects",
  author        = {Colin C. Caprani and Eugene J. OBrien},
  year          = "2010",
  language      = "English",
  isbn          = "9780415475570",
  pages         = "1 -- 8",
  editor        = "Hitoshi Furuta and Frangopol, {Dan M} and Masanobu Shinozuka",
  booktitle     = "Proceedings of the 10th International Conference on Structural Safety and Reliability (ICOSSAR2009)",
  publisher     = "CRC Press",
  url           = {https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.722.6789\&rep=rep1\&type=pdf}
}
@article{caprani2010gev,
  title         = {The use of predictive likelihood to estimate the distribution of extreme bridge traffic load effect},
  journal       = {Structural Safety},
  volume        = {32},
  number        = {2},
  pages         = {138--144},
  year          = {2010},
  issn          = {0167-4730},
  doi           = {https://doi.org/10.1016/j.strusafe.2009.09.001},
  url           = {https://www.sciencedirect.com/science/article/pii/S016747300900071X},
  author        = {Colin C. Caprani and Eugene J. OBrien}
}
@misc{card1993minimum,
  title         = {Minimum wages and employment: A case study of the fast food industry in New Jersey and Pennsylvania},
  author        = {Card, David and Krueger, Alan B},
  year          = {1993},
  publisher     = {National Bureau of Economic Research Cambridge, Mass., USA}
}
@misc{carpenter2016hierarchical,
  title         = {Hierarchical partial pooling for repeated binary trials},
  author        = {Carpenter, Bob and Gabry, J and Goodrich, B},
  year          = {2016},
  publisher     = {Technical report. Retrieved from https://mc-stan. org/users/docum entat ion~\ldots{}}
}
@article{ChernozhukovDoubleML,
  author        = {Victor, Chernozhukov and Denis, Chetverikov and Mert, Demirer and Esther, Duflo and Christian, Hansen and Whitney, Newey and James, Robins},
  title         = {{Double/debiased machine learning for treatment and structural parameters}},
  volume        = {21},
  journal       = {The Econometrics Journal},
  number        = {1},
  publisher     = {OUP},
  pages         = {1 -- 68},
  year          = {2018},
  doi           = {https://doi.org/10.1111/ectj.12097}
}
@book{coles2001gev,
  title         = "An introduction to statistical modeling of extreme values",
  author        = "Coles, Stuart",
  publisher     = "Springer",
  series        = "Springer Series in Statistics",
  edition       = 2001,
  month         = aug,
  year          = 2001,
  address       = "London, England",
  language      = "en",
  isbn          = {978-1-85233-459-8},
  url           = {https://doi.org/10.1007/978-1-4471-3675-0}
}
@book{collett2014survival,
  title         = {Modelling Survival Data in Medical Research},
  author        = {Collett, David},
  year          = {2014},
  publisher     = {CRC Press}
}
@article{collinswilson2019,
  title         = {Ten simple rules for the computational modeling of behavioral data},
  author        = {Wilson, Robert C and Collins, Anne GE},
  editor        = {Behrens, Timothy E},
  volume        = 8,
  year          = 2019,
  month         = {nov},
  pub_date      = {2019-11-26},
  pages         = {e49547},
  citation      = {eLife 2019;8:e49547},
  doi           = {10.7554/eLife.49547},
  abstract      = {Computational modeling of behavior has revolutionized psychology and neuroscience. By fitting models to experimental data we can probe the algorithms underlying behavior, find neural correlates of computational variables and better understand the effects of drugs, illness and interventions. But with great power comes great responsibility. Here, we offer ten simple rules to ensure that computational modeling is used with care and yields meaningful insights. In particular, we present a beginner-friendly, pragmatic and details-oriented introduction on how to relate models to data. What, exactly, can a model tell us about the mind? To answer this, we apply our rules to the simplest modeling techniques most accessible to beginning modelers and illustrate them with examples and code available online. However, most rules apply to more advanced techniques. Our hope is that by following our guidelines, researchers will avoid many pitfalls and unleash the power of computational modeling on their own data.},
  keywords      = {computational modeling, model fitting, validation, reproducibility},
  journal       = {eLife},
  issn          = {2050-084X},
  publisher     = {eLife Sciences Publications, Ltd}
}
@book{cunningham2021causal,
  title         = {Causal inference: The Mixtape},
  author        = {Cunningham, Scott},
  year          = {2021},
  publisher     = {Yale University Press}
}
@book{daniels2024bnp,
  title         = {Bayesian Nonparametrics for Causal Inference and Missing Data},
  author        = {Daniels, Michael and Linero, Antonio and Roy Jason},
  year          = {2024},
  publisher     = {CRC Press}
}
@article{Dickey1970,
  author        = {James M. Dickey and B. P. Lientz},
  title         = {{The Weighted Likelihood Ratio, Sharp Hypotheses about Chances, the Order of a Markov Chain}},
  volume        = {41},
  journal       = {The Annals of Mathematical Statistics},
  number        = {1},
  publisher     = {Institute of Mathematical Statistics},
  pages         = {214 -- 226},
  year          = {1970},
  doi           = {10.1214/aoms/1177697203}
}
@article{efron1975data,
  title         = {Data analysis using Stein's estimator and its generalizations},
  author        = {Efron, Bradley and Morris, Carl},
  journal       = {Journal of the American Statistical Association},
  volume        = {70},
  number        = {350},
  pages         = {311--319},
  year          = {1975},
  publisher     = {Taylor \& Francis}
}
@book{enders2022,
  title         = {Applied Missing Data Analysis},
  author        = {Enders K, Craig},
  year          = {2022},
  publisher     = {The Guilford Press}
}
@book{facure2023causal,
  title         = {Causal Inference in Python},
  author        = {Facure, Matheus},
  year          = {2023},
  publisher     = {O'Reilly}
}
@book{fox2010bayesian,
  title         = {Bayesian item response modeling: Theory and applications},
  author        = {Fox, Jean-Paul},
  year          = {2010},
  publisher     = {Springer}
}
@book{gelman2006data,
  title         = {Data analysis using regression and multilevel/hierarchical models},
  author        = {Gelman, Andrew and Hill, Jennifer},
  year          = {2006},
  publisher     = {Cambridge university press}
}
@article{gelman2006multilevel,
  title         = {Multilevel (hierarchical) modeling: what it can and cannot do},
  author        = {Gelman, Andrew},
  journal       = {Technometrics},
  volume        = {48},
  number        = {3},
  pages         = {432--435},
  year          = {2006},
  publisher     = {Taylor \& Francis}
}
@article{gelman2008scaling,
  title         = {Scaling regression inputs by dividing by two standard deviations},
  author        = {Gelman, Andrew},
  journal       = {Statistics in medicine},
  volume        = {27},
  number        = {15},
  pages         = {2865--2873},
  year          = {2008},
  publisher     = {Wiley Online Library},
  doi           = {10.1002/sim.3107}
}
@book{gelman2013bayesian,
  title         = {Bayesian Data Analysis},
  publisher     = {Chapman and Hall/CRC},
  author        = {Gelman, Andrew and Carlin, John B. and Stern, Hal S. and Dunson, David B. and Vehtari, Aki and Rubin, Donald B.},
  year          = {2013}
}
@article{gelman2020bayesian,
  title         = {Bayesian workflow},
  author        = {Gelman, Andrew and Vehtari, Aki and Simpson, Daniel and Margossian, Charles C and Carpenter, Bob and Yao, Yuling and Kennedy, Lauren and Gabry, Jonah and B{\"u}rkner, Paul-Christian and Modr{\'a}k, Martin},
  journal       = {arXiv preprint arXiv:2011.01808},
  year          = {2020},
  url           = {https://arxiv.org/abs/2011.01808}
}
@book{gelman2020regression,
  title         = {Regression and other stories},
  author        = {Gelman, Andrew and Hill, Jennifer and Vehtari, Aki},
  year          = {2020},
  publisher     = {Cambridge University Press}
}
@article{goldberg2001eigentaste,
  author        = {Ken Goldberg and Theresa Roeder and Chris Perkins},
  title         = {Eigentaste: A Constant Time Collaborative Filtering Algorithm},
  journal       = {Information Retrieval},
  year          = {2001},
  volume        = {4},
  pages         = {133--151}
}
@article{goodman1999,
  doi           = {10.7326/0003-4819-130-12-199906150-00008},
  url           = {https://doi.org/10.7326/0003-4819-130-12-199906150-00008},
  year          = {1999},
  month         = jun,
  publisher     = {American College of Physicians},
  volume        = {130},
  number        = {12},
  pages         = {995},
  author        = {Steven N. Goodman},
  title         = {Toward Evidence-Based Medical Statistics. 1: The P Value Fallacy},
  journal       = {Annals of Internal Medicine}
}
@article{google_causal_impact2015,
  title         = {Inferring causal impact using Bayesian structural time-series models},
  author        = {Kay H. Brodersen and Fabian Gallusser and Jim Koehler and Nicolas Remy and Steven L. Scott},
  year          = {2015},
  journal       = {Annals of Applied Statistics},
  pages         = {247--274},
  volume        = {9}
}
@misc{harper2015movielens,
  title         = {The MovieLens Datasets: History and Context},
  author        = {Harper, F. Maxwell and Konstan, Joseph A.},
  journal       = {ACM Transactions on Interactive Intelligent Systems},
  volume        = {5},
  number        = {4},
  pages         = {1--19},
  year          = {2016},
  month         = {January},
  url           = {https://doi.org/10.1145/2827872}
}
@book{hayes2017introduction,
  title         = {Introduction to mediation, moderation, and conditional process analysis: A regression-based approach},
  author        = {Hayes, Andrew F},
  year          = {2017},
  publisher     = {Guilford publications}
}
@book{hernan2020whatif,
  title         = {Causal Inference: What If},
  author        = {Hern\'{a}n, MA and Robins, JM},
  year          = {2020},
  publisher     = {Chapman \& Hall/CRC}
}
@article{hoffman2014nuts,
  title         = {The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo},
  author        = {Hoffman, Matthew and Gelman, Andrew},
  year          = {2014},
  journal       = {Journal of Machine Learning Research},
  volume        = {15},
  issue         = {1},
  pages         = {1593--1623},
  url           = {https://dl.acm.org/doi/10.5555/2627435.2638586}
}
@misc{hogg2010data,
  title         = {Data analysis recipes: Fitting a model to data},
  author        = {David W. Hogg and Jo Bovy and Dustin Lang},
  year          = {2010},
  eprint        = {1008.4686},
  archiveprefix = {arXiv},
  primaryclass  = {astro-ph.IM}
}
@book{huntington2021effect,
  title         = {The effect: An introduction to research design and causality},
  author        = {Huntington-Klein, Nick},
  year          = {2021},
  publisher     = {Chapman and Hall/CRC}
}
@online{Huszár2019causal2,
  author        = {Husz\'{a}r, Ferenc},
  title         = {Causal Inference 2: Illustrating Interventions via a Toy Example},
  year          = {2019},
  url           = {https://www.inference.vc/causal-inference-2-illustrating-interventions-in-a-toy-example/},
  urldate       = {2023-07-01}
}
@article{iacobucci2016mean,
  title         = {Mean centering helps alleviate ``micro'' but not ``macro'' multicollinearity},
  author        = {Iacobucci, Dawn and Schneider, Matthew J and Popovich, Deidre L and Bakamitsos, Georgios A},
  journal       = {Behavior research methods},
  volume        = {48},
  number        = {4},
  pages         = {1308--1317},
  year          = {2016},
  publisher     = {Springer}
}
@article{iacobucci2017mean,
  title         = {Mean centering, multicollinearity, and moderators in multiple regression: The reconciliation redux},
  author        = {Iacobucci, Dawn and Schneider, Matthew J and Popovich, Deidre L and Bakamitsos, Georgios A},
  journal       = {Behavior research methods},
  volume        = {49},
  number        = {1},
  pages         = {403--404},
  year          = {2017},
  publisher     = {Springer}
}
@book{ivezić2014astroMLtext,
  author        = {\v{Z}eljko Ivezi\'{c} and Andrew J. Connolly and Jacob T. VanderPlas and Alexander Gray},
  doi           = {10.1515/9781400848911},
  title         = {Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data},
  year          = {2014},
  publisher     = {Princeton University Press},
  isbn          = {9781400848911}
}
@book{james2021statisticallearning,
  title         = {An Introduction to Statistical Learning},
  author        = {James, Gareth ad Witten, Daniela and Hastie, Trevor and Tibshirani, Robert},
  year          = {2021},
  publisher     = {Springer},
  doi           = {https://doi.org/10.1007/978-1-0716-1418-1},
  issn          = {1431-875X},
  isbn          = {978-1-0716-1420-4}
}
@article{johnson1999,
  doi           = {10.2307/3802789},
  url           = {https://doi.org/10.2307/3802789},
  year          = {1999},
  month         = jul,
  publisher     = {{JSTOR}},
  volume        = {63},
  number        = {3},
  pages         = {763},
  author        = {Douglas H. Johnson},
  title         = {The Insignificance of Statistical Significance Testing},
  journal       = {The Journal of Wildlife Management}
}
@online{junpenglao2020,
  title         = {Partial Missing Multivariate Observation and What to Do With Them},
  author        = {Lao, Junpeng},
  url           = {https://discourse.pymc.io/t/partial-missing-multivariate-observation-and-what-to-do-with-them-by-junpeng-lao/6050},
  urldate       = {2020-10-01},
  year          = {2020}
}
@manual{kassambara2019datarium,
  title         = {datarium: Data Bank for Statistical Analysis and Visualization},
  author        = {Alboukadel Kassambara},
  year          = {2019},
  note          = {R package version 0.1.0},
  url           = {https://CRAN.R-project.org/package=datarium}
}
@book{kemp1985atlas,
  author        = {Kemp, I. and World Health Organization. and International Agency for Research on Cancer.},
  title         = {Atlas of cancer in Scotland, 1975-1980 : incidence and epidemiological perspective},
  publisher     = {World Health Organization ; Distributed for the International Agency for Research on Cancer by Oxford University Press] Lyon : [Oxford, UK ; New York},
  pages         = {282 p. :},
  year          = {1985},
  type          = {Book}
}
@misc{kingma2014autoencoding,
  title         = {Auto-Encoding Variational Bayes},
  author        = {Diederik P Kingma and Max Welling},
  year          = {2014},
  eprint        = {1312.6114},
  archiveprefix = {arXiv},
  primaryclass  = {stat.ML}
}
@article{koren2009matrixfactorization,
  author        = {Koren, Yehuda and Bell, Robert and Volinsky, Chris},
  journal       = {Computer},
  title         = {Matrix Factorization Techniques for Recommender Systems},
  year          = {2009},
  volume        = {42},
  number        = {8},
  pages         = {30--37},
  doi           = {10.1109/MC.2009.263}
}
@article{kruschke2011bayesian,
  title         = {Bayesian assessment of null values via parameter estimation and model comparison},
  author        = {Kruschke, John K},
  journal       = {Perspectives on Psychological Science},
  volume        = {6},
  number        = {3},
  pages         = {299--312},
  year          = {2011},
  publisher     = {Sage Publications Sage CA: Los Angeles, CA}
}
@article{kruschke2013,
  doi           = {10.1037/a0029146},
  url           = {https://doi.org/10.1037/a0029146},
  year          = {2013},
  publisher     = {American Psychological Association ({APA})},
  volume        = {142},
  number        = {2},
  pages         = {573--603},
  author        = {John K. Kruschke},
  title         = {Bayesian estimation supersedes the t test.},
  journal       = {Journal of Experimental Psychology: General}
}
@book{kruschke2014doing,
  title         = {Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan},
  author        = {Kruschke, John},
  year          = {2014},
  publisher     = {Academic Press}
}
@misc{kucukelbir2015automatic,
  title         = {Automatic Variational Inference in Stan},
  author        = {Alp Kucukelbir and Rajesh Ranganath and Andrew Gelman and David M. Blei},
  year          = {2015},
  eprint        = {1506.03431},
  archiveprefix = {arXiv},
  primaryclass  = {stat.ML}
}
@book{kurzAppliedLongitudinalDataAnalysis2021,
  title         = {Applied longitudinal data analysis in brms and the tidyverse},
  author        = {Kurz, A. Solomon},
  year          = {2021},
  publisher     = {Bookdown},
  url           = {https://bookdown.org/content/4253/}
}
@incollection{leroux2000estimation,
  title         = {Estimation of disease rates in small areas: a new mixed model for spatial dependence},
  author        = {Leroux, Brian G and Lei, Xingye and Breslow, Norman},
  booktitle     = {Statistical models in epidemiology, the environment, and clinical trials},
  pages         = {179--191},
  year          = {2000},
  publisher     = {Springer}
}
@book{levy2020bayesian,
  title         = {Bayesian Psychometric Modeling},
  author        = {Levy, Roy and Mislevy, Robert J},
  year          = {2020},
  publisher     = {CRC Press}
}
@article{lewandowski2009generating,
  title         = {Generating random correlation matrices based on vines and extended onion method},
  author        = {Lewandowski, Daniel and Kurowicka, Dorota and Joe, Harry},
  journal       = {Journal of multivariate analysis},
  volume        = {100},
  number        = {9},
  pages         = {1989--2001},
  year          = {2009},
  publisher     = {Elsevier}
}
@article{LIDDELL2018328,
  title         = {Analyzing ordinal data with metric models: What could possibly go wrong?},
  author        = {Torrin M. Liddell and John K. Kruschke},
  journal       = {Journal of Experimental Social Psychology},
  volume        = {79},
  year          = {2018}
}
@book{martin2018bayesian,
  title         = {Bayesian analysis with Python: introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ},
  author        = {Martin, Osvaldo},
  year          = {2018},
  publisher     = {Packt Publishing Ltd}
}
@book{martin2021bayesian,
  title         = {Bayesian Modeling and Computation in Python},
  author        = {Martin, Osvaldo A and Kumar, Ravin and Lao, Junpeng},
  year          = {2021},
  publisher     = {Chapman and Hall/CRC},
  doi           = {10.1201/9781003019169}
}
@article{mcclelland2017multicollinearity,
  title         = {Multicollinearity is a red herring in the search for moderator variables: A guide to interpreting moderated multiple regression models and a critique of Iacobucci, Schneider, Popovich, and Bakamitsos (2016)},
  author        = {McClelland, Gary H and Irwin, Julie R and Disatnik, David and Sivan, Liron},
  journal       = {Behavior research methods},
  volume        = {49},
  number        = {1},
  pages         = {394--402},
  year          = {2017},
  publisher     = {Springer}
}
@book{mcelreath2018statistical,
  title         = {Statistical rethinking: A Bayesian course with examples in R and Stan},
  author        = {McElreath, Richard},
  year          = {2018},
  publisher     = {Chapman and Hall/CRC}
}
@book{mcknulty2020people,
  title         = {Handbook of Regression Modeling in People Analytics},
  author        = {McKnulty, Keith},
  year          = {2020},
  publisher     = {Chapman and Hall/CRC}
}
@book{Meeker2021,
  author        = {Meeker, W.Q. and Escobar, L.A. and Pascual, F.G},
  publisher     = {Wiley},
  title         = {Statistical Methods for Reliability Data},
  year          = {2022}
}
@inproceedings{mnih2008advances,
  title         = {Probabilistic Matrix Factorization},
  author        = {Mnih, Andriy and Salakhutdinov, Russ R},
  booktitle     = {Advances in Neural Information Processing Systems},
  editor        = {J. Platt and D. Koller and Y. Singer and S. Roweis},
  publisher     = {Curran Associates, Inc.},
  url           = {https://proceedings.neurips.cc/paper/2007/file/d7322ed717dedf1eb4e6e52a37ea7bcd-Paper.pdf},
  volume        = {20},
  year          = {2008}
}
@misc{mnih2013playing,
  title         = {Playing Atari with Deep Reinforcement Learning},
  author        = {Volodymyr Mnih and Koray Kavukcuoglu and David Silver and Alex Graves and Ioannis Antonoglou and Daan Wierstra and Martin Riedmiller},
  year          = {2013},
  eprint        = {1312.5602},
  archiveprefix = {arXiv},
  primaryclass  = {cs.LG}
}
@book{molak2023ciadip,
  title         = {Causal Inference and Discovery in Python},
  author        = {Molak, Aleksander},
  year          = {2023},
  publisher     = {Packt Publishing}
}
@book{molnar2019,
  title         = {Interpretable Machine Learning},
  author        = {Christoph Molnar},
  year          = {2019},
  subtitle      = {A Guide for Making Black Box Models Explainable},
  url           = {https://christophm.github.io/interpretable-ml-book/},
  publisher     = {Christoph Molnar}
}
@article{morris2021bym,
  title         = {Bayesian hierarchical spatial models: Implementing the Besag York Molli\'{e} model in stan},
  author        = {Mitzi Morris and Katherine Wheeler-Martin and Dan Simpson and Stephen J. Mooney and Andrew Gelman and Charles DiMaggio},
  journal       = {Spatial and Spatio-temporal Epidemiology},
  volume        = {31},
  pages         = {100301},
  year          = {2019},
  issn          = {1877-5845},
  doi           = {https://doi.org/10.1016/j.sste.2019.100301},
  url           = {https://www.sciencedirect.com/science/article/pii/S1877584518301175}
}
@article{nowlan1992simplifying,
  title         = {Simplifying Neural Networks By Soft Weight-Sharing},
  author        = {Nowlan, Steven J and Hinton, Geoffrey E},
  journal       = {Neural computation},
  volume        = {4},
  number        = {4},
  pages         = {473--493},
  year          = {1992},
  publisher     = {MIT Press}
}
@article{nuijten2015default,
  title         = {A default Bayesian hypothesis test for mediation},
  author        = {Nuijten, Mich{\`e}le B and Wetzels, Ruud and Matzke, Dora and Dolan, Conor V and Wagenmakers, Eric-Jan},
  journal       = {Behavior research methods},
  volume        = {47},
  number        = {1},
  pages         = {85--97},
  year          = {2015},
  publisher     = {Springer}
}
@online{numpyroBirthdays,
  title         = {Example: Hilbert space approximation for Gaussian processes},
  url           = {https://num.pyro.ai/en/stable/examples/hsgp.html}
}
@online{orduz2024Birthdays,
  title         = {Time Series Modeling with HSGP: Baby Births Example},
  author        = {Orduz, Juan},
  url           = {https://juanitorduz.github.io/birthdays/},
  urldate       = {2024-01-02},
  year          = {2024}
}
@unpublished{padonou2015polar,
  title         = {{Polar Gaussian Processes for Predicting on Circular Domains}},
  author        = {Padonou, Esp{\'e}ran and Roustant, O},
  url           = {https://hal.archives-ouvertes.fr/hal-01119942},
  note          = {working paper or preprint},
  year          = {2015},
  month         = Feb,
  pdf           = {https://hal.archives-ouvertes.fr/hal-01119942v1/file/PolarGP_CircularDomains.pdf}
}
@book{pearl1985prob,
  title         = {Probabilistic Reasoning in Intelligent Systems: Networks of plausible Inference},
  author        = {Pearl, Judea},
  publisher     = {Morgan Kaufman},
  year          = {1985}
}
@book{pearl2000causality,
  title         = {Causality: Models, reasoning and inference},
  author        = {Pearl, Judea},
  publisher     = {Cambridge University Press},
  year          = {2000},
  isbn          = {978-0521895606}
}
@book{pearl2016causal,
  title         = {Causal inference in statistics: A primer},
  author        = {Glymour, Madelyn and Pearl, Judea and Jewell, Nicholas P},
  year          = {2016},
  publisher     = {John Wiley \& Sons}
}
@book{pearl2018why,
  title         = {The book of why: the new science of cause and effect},
  author        = {Pearl, Judea and Mackenzie, Dana},
  year          = {2018},
  publisher     = {Basic books}
}
@article{penrose1985,
  title         = {{GENERALIZED} {BODY} {COMPOSITION} {PREDICTION} {EQUATION} {FOR} {MEN} {USING} {SIMPLE} {MEASUREMENT} {TECHNIQUES}},
  volume        = {17},
  issn          = {0195-9131},
  url           = {https://journals.lww.com/acsm-msse/citation/1985/04000/generalized_body_composition_prediction_equation.37.aspx},
  abstract      = {An abstract is unavailable. This article is available as a PDF only.},
  language      = {en-US},
  number        = {2},
  urldate       = {2023-10-17},
  journal       = {Medicine \& Science in Sports \& Exercise},
  author        = {Penrose, K. W. and Nelson, A. G. and Fisher, A. G.},
  month         = apr,
  year          = {1985},
  pages         = {189}
}
@misc{quiroga2022bart,
  title         = {Bayesian additive regression trees for probabilistic programming},
  author        = {Quiroga, Miriana and Garay, Pablo G and Alonso, Juan M. and Loyola, Juan Martin and Martin, Osvaldo A},
  publisher     = {arXiv},
  doi           = {10.48550/ARXIV.2206.03619},
  url           = {https://arxiv.org/abs/2206.03619},
  keywords      = {Computation (stat.CO), FOS: Computer and information sciences, FOS: Computer and information sciences},
  year          = {2022},
  copyright     = {Creative Commons Attribution Share Alike 4.0 International}
}
@book{rasmussen2003gaussian,
  title         = {Gaussian Processes for Machine Learning},
  author        = {Rasmussen, Carl Edward and Williams, Christopher K. I.},
  year          = {2006},
  isbn          = {026218253X},
  publisher     = {The MIT Press},
  url           = {https://gaussianprocess.org/gpml/}
}
@article{riebler2016intuitive,
  title         = {An intuitive Bayesian spatial model for disease mapping that accounts for scaling},
  author        = {Riebler, Andrea and S{\o}rbye, Sigrunn H and Simpson, Daniel and Rue, H{\aa}vard},
  journal       = {Statistical methods in medical research},
  volume        = {25},
  number        = {4},
  pages         = {1145--1165},
  year          = {2016},
  publisher     = {Sage Publications Sage UK: London, England}
}
@article{riutort2022PracticalHilbertSpaceApproximate,
  author        = {Riutort-Mayol, Gabriel and B{\"u}rkner, Paul-Christian and Andersen, Michael R. and Solin, Arno and Vehtari, Aki},
  doi           = {10.1007/s11222-022-10167-2},
  journal       = {Statistics and Computing},
  number        = {1},
  pages         = {17},
  title         = {Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming},
  url           = {https://doi.org/10.1007/s11222-022-10167-2},
  volume        = {33},
  year          = {2022}
}
@book{roback2021beyond,
  title         = {Beyond multiple linear regression: Applied generalized linear models and multilevel models in R},
  author        = {Roback, P., and Legler, J.},
  year          = {2021},
  publisher     = {CRC Press},
  url           = {https://bookdown.org/roback/bookdown-BeyondMLR/}
}
@online{rochford2018,
  author        = {Austin Rochford},
  url           = {https://austinrochford.com/posts/2018-11-10-monotonic-predictors.html}
}
@inproceedings{salakhutdinov2008bayesian,
  title         = {Bayesian Probabilistic Matrix Factorization Using Markov Chain Monte Carlo},
  author        = {Salakhutdinov, Ruslan and Mnih, Andriy},
  booktitle     = {Proceedings of the 25th international conference on Machine learning},
  pages         = {880--887},
  year          = {2008},
  volume        = {25}
}
@article{silver2016masteringgo,
  title         = {Mastering the game of Go with deep neural networks and tree search},
  author        = {D. Silver, A. Huang, C. Maddison et al.},
  journal       = {Nature},
  volume        = {529},
  pages         = {484--489},
  year          = {2016},
  url           = {https://doi.org/10.1038/nature16961}
}
@book{singer2003,
  title         = {Applied Longitudinal Data Analysis},
  author        = {Singer .D, Judith and Willett .B, John},
  year          = {2003},
  publisher     = {Oxford University Press}
}
@article{solin2020Hilbert,
  author        = {Solin, Arno and S{\"a}rkk{\"a}, Simo},
  doi           = {10.1007/s11222-019-09886-w},
  journal       = {Statistics and Computing},
  number        = {2},
  pages         = {419--446},
  title         = {Hilbert space methods for reduced-rank Gaussian process regression},
  url           = {https://doi.org/10.1007/s11222-019-09886-w},
  volume        = {30},
  year          = {2020}
}
@article{spiller2013spotlights,
  title         = {Spotlights, floodlights, and the magic number zero: Simple effects tests in moderated regression},
  author        = {Spiller, Stephen A and Fitzsimons, Gavan J and Lynch Jr, John G and McClelland, Gary H},
  journal       = {Journal of marketing research},
  volume        = {50},
  number        = {2},
  pages         = {277--288},
  year          = {2013},
  publisher     = {SAGE Publications Sage CA: Los Angeles, CA}
}
@online{stucchio2015bayesian,
  title         = {Bayesian A/B Testing at VWO},
  author        = {Stucchio, Chris},
  year          = {2015},
  url           = {https://vwo.com/downloads/VWO_SmartStats_technical_whitepaper.pdf}
}
@misc{szegedy2014going,
  title         = {Going Deeper with Convolutions},
  author        = {Christian Szegedy and Wei Liu and Yangqing Jia and Pierre Sermanet and Scott Reed and Dragomir Anguelov and Dumitru Erhan and Vincent Vanhoucke and Andrew Rabinovich},
  year          = {2014},
  eprint        = {1409.4842},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV}
}
@online{Talebi2022dooperator,
  author        = {Talebi, Shawhin},
  title         = {Causal Effects via the Do-operator},
  year          = {2022},
  url           = {https://towardsdatascience.com/causal-effects-via-the-do-operator-5415aefc834a},
  urldate       = {2023-07-01}
}
@article{taylor2018forecasting,
  title         = {Forecasting at scale},
  author        = {Taylor, Sean J and Letham, Benjamin},
  journal       = {The American Statistician},
  volume        = {72},
  number        = {1},
  pages         = {37--45},
  year          = {2018},
  publisher     = {Taylor \& Francis},
  url           = {https://peerj.com/preprints/3190/}
}
@article{terBraak2006markov,
  title         = {A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces},
  author        = {ter Braak, Cajo J.F.},
  journal       = {Statistics and Computing},
  number        = {16},
  pages         = {239--249},
  year          = {2006},
  publisher     = {Springer},
  url           = {https://link.springer.com/article/10.1007/s11222-006-8769-1}
}
@article{terBraak2008differential,
  title         = {Differential Evolution Markov Chain with snooker updater and fewer chains},
  author        = {ter Braak, Cajo J.F. and Vrugt, Jasper A.},
  journal       = {Statistics and Computing},
  number        = {18},
  pages         = {435--446},
  year          = {2008},
  publisher     = {Springer},
  url           = {https://link.springer.com/content/pdf/10.1007/s11222-008-9104-9.pdf?pdf=button}
}
@book{train2009,
  title         = {Discrete Choice Methods with Simulation},
  author        = {Train, Kenneth},
  year          = {2009},
  publisher     = {Cambridge}
}
@online{vandenbergSPSS,
  author        = {van den Berg, R. G},
  title         = {SPSS Moderation Regression Tutorial},
  url           = {https://www.spss-tutorials.com/spss-regression-with-moderation-interaction-effect/},
  urldate       = {2022-03-20}
}
@inproceedings{vanderplas2012astroML,
  author        = {{Vanderplas}, J.T. and {Connolly}, A.J. and {Ivezi{\'c}}, {\v Z}. and {Gray}, A.},
  booktitle     = {Conference on Intelligent Data Understanding (CIDU)},
  title         = {Introduction to astroML: Machine learning for astrophysics},
  month         = {oct.},
  pages         = {47--54},
  doi           = {10.1109/CIDU.2012.6382200},
  year          = {2012}
}
@online{vehtari2022Birthdays,
  title         = {Bayesian workflow book - Birthdays},
  author        = {Vehtari, Aki},
  url           = {https://avehtari.github.io/casestudies/Birthdays/birthdays.html},
  urldate       = {2022-03-07},
  year          = {2022}
}
@online{vieira2022BVAR,
  title         = {Bayesian Vector Autoregression in PyMC},
  author        = {Vieira, Ricardo},
  url           = {https://www.pymc-labs.io/blog-posts/bayesian-vector-autoregression/},
  urldate       = {2022-12-09}
}
@article{Wagenmakers2010,
  title         = {Bayesian hypothesis testing for psychologists: A tutorial on the Savage–Dickey method},
  journal       = {Cognitive Psychology},
  volume        = {60},
  number        = {3},
  pages         = {158--189},
  year          = {2010},
  issn          = {0010-0285},
  doi           = {https://doi.org/10.1016/j.cogpsych.2009.12.001},
  url           = {https://www.sciencedirect.com/science/article/pii/S0010028509000826},
  author        = {Eric-Jan Wagenmakers and Tom Lodewyckx and Himanshu Kuriyal and Raoul Grasman}
}
@book{wilkinson2005grammar,
  title         = {The Grammar of Graphics},
  author        = {Wilkinson, Leland},
  year          = {2005},
  publisher     = {Springer},
  doi           = {https://doi.org/10.1007/0-387-28695-0},
  issn          = {1431-8784},
  isbn          = {978-0-387-24544-7}
}
@article{wing2018designing,
  title         = {Designing difference in difference studies: best practices for public health policy research},
  author        = {Wing, Coady and Simon, Kosali and Bello-Gomez, Ricardo A},
  journal       = {Annu Rev Public Health},
  volume        = {39},
  number        = {1},
  pages         = {453--469},
  year          = {2018}
}
@article{Yao_2018,
  doi           = {10.1214/17-ba1091},
  url           = {https://doi.org/10.1214\%2F17-ba1091},
  year          = 2018,
  month         = {sep},
  publisher     = {Institute of Mathematical Statistics},
  volume        = {13},
  number        = {3},
  author        = {Yuling Yao and Aki Vehtari and Daniel Simpson and Andrew Gelman},
  title         = {Using Stacking to Average Bayesian Predictive Distributions (with Discussion)},
  journal       = {Bayesian Analysis}
}
@article{Yao_2022,
  author        = {Yuling Yao and Gregor Pir\v{s} and Aki Vehtari and Andrew Gelman},
  title         = {{Bayesian Hierarchical Stacking: Some Models Are (Somewhere) Useful}},
  volume        = {17},
  journal       = {Bayesian Analysis},
  number        = {4},
  publisher     = {International Society for Bayesian Analysis},
  pages         = {1043 -- 1071},
  keywords      = {Bayesian hierarchical modeling, conditional prediction, covariate shift, model averaging, prior construction, stacking},
  year          = {2022},
  doi           = {10.1214/21-BA1287},
  url           = {https://doi.org/10.1214/21-BA1287}
}
@article{yuan2009bayesian,
  title         = {Bayesian mediation analysis.},
  author        = {Yuan, Ying and MacKinnon, David P},
  journal       = {Psychological methods},
  volume        = {14},
  number        = {4},
  pages         = {301},
  year          = {2009},
  publisher     = {American Psychological Association}
}
@article{zhang2017moderation,
  title         = {Moderation analysis with missing data in the predictors.},
  author        = {Zhang, Qian and Wang, Lijuan},
  journal       = {Psychological methods},
  volume        = {22},
  number        = {4},
  pages         = {649},
  year          = {2017},
  publisher     = {American Psychological Association}
}
@article{zhang2021pathfinder,
  title         = {Pathfinder: Parallel quasi-Newton variational inference},
  author        = {Zhang, Lu and Carpenter, Bob and Gelman, Andrew and Vehtari, Aki},
  journal       = {arXiv preprint arXiv:2108.03782},
  year          = {2021}
}