Bibliografia#
Jim Albert and Jingchen Hu. Probability and Bayesian Modeling. Chapman and Hall/CRC, 2019.
Craig A Anderson and Karen E Dill. Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. Journal of Personality and Social Psychology, 78(4):772–790, 2000.
J Scott Armstrong and Kesten C Green. The Scientific Method: A Guide to Finding Useful Knowledge. Cambridge University Press, 2022.
Monya Baker. Reproducibility crisis. Nature, 533(26):353–66, 2016.
Beth Baribault and Anne GE Collins. Troubleshooting bayesian cognitive models. Psychological Methods, 2023.
Michael Betancourt. Diagnosing suboptimal cotangent disintegrations in hamiltonian monte carlo. arXiv preprint arXiv:1604.00695, 2016.
Peter J Bickel, Eugene A Hammel, and J William O'Connell. Sex bias in graduate admissions: data from berkeley: measuring bias is harder than is usually assumed, and the evidence is sometimes contrary to expectation. Science, 187(4175):398–404, 1975.
Emile Borel. Introduction Géométrique. G. Villars, New York, 1914.
George EP Box. Sampling and bayes’ inference in scientific modelling and robustness. Journal of the Royal Statistical Society Series A: Statistics in Society, 143(4):383–404, 1980.
Kay H Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, and Steven L Scott. Inferring causal impact using bayesian structural time-series models. Annals of Applied Statistics, 9:247–274, 2015.
Stephen P Brooks. Bayesian computation: a statistical revolution. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 361(1813):2681–2697, 2003.
Naomi C Brownstein, Thomas A Louis, Anthony O’Hagan, and Jane Pendergast. The role of expert judgment in statistical inference and evidence-based decision-making. The American Statistician, 73(sup1):56–68, 2019.
Bob Carpenter, Andrew Gelman, Matthew D Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell. Stan: a probabilistic programming language. Journal of statistical software, 76(1):1–32, 2017.
Aubrey Clayton. Bernoulli's Fallacy: Statistical Illogic and the Crisis of Modern Science. Columbia University Press, 2021.
Bruno De Finetti. Theory of probability: A critical introductory treatment. Volume 6. John Wiley & Sons, 2017.
Van Rens de Schoot, Duco Veen, Laurent Smeets, and Sonja Winter. A tutorial on using the wambs checklist to avoid the misuse of bayesian statistics. 2020.
Allen B Downey. Think Bayes. " O'Reilly Media, Inc.", 2021.
Simon Duane, Anthony D Kennedy, Brian J Pendleton, and Duncan Roweth. Hybrid monte carlo. Physics letters B, 195(2):216–222, 1987.
Lucy D’Agostino McGowan, Travis Gerke, and Malcolm Barrett. Causal inference is not just a statistics problem. Journal of Statistics and Data Science Education, pages 1–6, 2024.
Alexander Etz, Quentin F Gronau, Fabian Dablander, Peter A Edelsbrunner, and Beth Baribault. How to become a bayesian in eight easy steps: an annotated reading list. Psychonomic bulletin & review, 25(1):219–234, 2018.
Peter C Fishburn. The axioms of subjective probability. Statistical Science, 1(3):335–345, 1986.
David R Gagnon, Susan Doron-LaMarca, Margret Bell, Timothy J O'Farrell, and Casey T Taft. Poisson regression for modeling count and frequency outcomes in trauma research. Journal of Traumatic Stress, 21(5):448–454, 2008.
Andrew Gelman. Commentary on “crisis in science? or crisis in statistics! mixed messages in statistics with impact on science”. Journal of Statistical Research, 48-50(1):11–12, 2016.
Andrew Gelman and John Carlin. Beyond power calculations: assessing type s (sign) and type m (magnitude) errors. Perspectives on Psychological Science, 9(6):641–651, 2014.
Andrew Gelman, John B Carlin, Hal S Stern, and Donald B Rubin. Bayesian data analysis. Chapman and Hall/CRC, 1995.
Andrew Gelman, Jennifer Hill, and Aki Vehtari. Regression and other stories. Cambridge University Press, 2020.
Andrew Gelman and Cosma Rohilla Shalizi. Philosophy and the practice of bayesian statistics. British Journal of Mathematical and Statistical Psychology, 66(1):8–38, 2013.
Andrew Gelman, Daniel Simpson, and Michael Betancourt. The prior can often only be understood in the context of the likelihood. Entropy, 19(10):555, 2017.
Andrew Gelman, Aki Vehtari, Daniel Simpson, Charles C Margossian, Bob Carpenter, Yuling Yao, Lauren Kennedy, Jonah Gabry, Paul-Christian Bürkner, and Martin Modrák. Bayesian workflow. arXiv preprint arXiv:2011.01808, 2020.
Stuart Geman and Donald Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on pattern analysis and machine intelligence, 6:721–741, 1984.
John V Guttag. Introduction to computation and programming using Python. Mit Press, 2021.
W. Keith Hastings. Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1):97–109, 1970.
Rink Hoekstra, Richard D Morey, Jeffrey N Rouder, and Eric-Jan Wagenmakers. Robust misinterpretation of confidence intervals. Psychonomic Bulletin & Review, 21(5):1157–1164, 2014.
Matthew D Hoffman, Andrew Gelman, and others. The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo. Journal of Machine Learning Research, 15(1):1593–1623, 2014.
Colin Howson and Peter Urbach. Scientific reasoning: the Bayesian approach. Open Court Publishing, 2006.
Edwin T Jaynes. Probability theory: The logic of science. Cambridge University Press, 2003.
Alicia A. Johnson, Miles Ott, and Mine Dogucu. Bayes Rules! An Introduction to Bayesian Modeling with R. CRC Press, 2022.
David Kaplan. Bayesian statistics for the social sciences. Guilford Publications, 2023.
John Kruschke. Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press, 2014.
Dennis V Lindley. Understanding uncertainty. John Wiley & Sons, 2013.
Eric Loken and Andrew Gelman. Measurement error and the replication crisis. Science, 355(6325):584–585, 2017.
Karsten Lübke, Matthias Gehrke, Jörg Horst, and Gero Szepannek. Why we should teach causal inference: examples in linear regression with simulated data. Journal of Statistics Education, 28(2):133–139, 2020.
Osvaldo Martin. Bayesian analysis with python. Packt Publishing Ltd, 2024.
Osvaldo A Martin, Ravin Kumar, and Junpeng Lao. Bayesian Modeling and Computation in Python. CRC Press, 2022.
Richard McElreath. Statistical rethinking: A Bayesian course with examples in R and Stan. CRC Press, Boca Raton, Florida, 2nd edition edition, 2020.
Wes McKinney. Python for Data Analysis. " O'Reilly Media, Inc.", 2022.
S. A. Mehr, L. A. Song, and E. S. Spelke. For 5-month-old infants, melodies are social. Psychological Science, 27(4):486–501, 2016.
Nicholas Metropolis, Arianna W. Rosenbluth, Marshall N. Rosenbluth, Augusta H. Teller, and Edward Teller. Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6):1087–1092, 1953.
Stanley Milgram. Behavioral study of obedience. The Journal of Abnormal and Social Psychology, 67(4):371–378, 1963.
Regina Nuzzo. Statistical errors. Nature, 506(7487):150–152, 2014.
Anthony O'Hagan. Expert knowledge elicitation: subjective but scientific. The American Statistician, 73(sup1):69–81, 2019.
Judea Pearl. Causality. Cambridge University Press, 2009.
S James Press. Subjective and objective Bayesian statistics: Principles, models, and applications. John Wiley & Sons, 2009.
Michael L Radelet and Glenn L Pierce. Choosing those who will die: race and the death penalty in florida. Florida Law Review, 43:1–34, 1991.
Frank P Ramsey. Truth and probability. In Readings in Formal Epistemology: Sourcebook, pages 21–45. Springer, 1926.
Julia M Rohrer. Thinking clearly about correlations and causation: graphical causal models for observational data. Advances in methods and practices in psychological science, 1(1):27–42, 2018.
Linda Rosa, Emily Rosa, Larry Sarner, and Stephen Barrett. A close look at therapeutic touch. Jama, 279(13):1005–1010, 1998.
Marshall Sahlins. Stone age economics. Routledge, 2013.
Edward H Simpson. The interpretation of interaction in contingency tables. Journal of the Royal Statistical Society: Series B (Methodological), 13(2):238–241, 1951.
Stanley Smith Stevens. On the theory of scales of measurement. Science, 103(2684):677–680, 1946.
José Unpingco. Python for probability, statistics, and machine learning. Volume 1. Springer, 2022.
Rens Van De Schoot, Sonja D Winter, Oisín Ryan, Mariëlle Zondervan-Zwijnenburg, and Sarah Depaoli. A systematic review of bayesian articles in psychology: the last 25 years. Psychological Methods, 22(2):217–239, 2017.
Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, and Paul-Christian Bürkner. Rank-normalization, folding, and localization: an improved r ̂ for assessing convergence of mcmc (with discussion). Bayesian analysis, 16(2):667–718, 2021.
Eric-Jan Wagenmakers, Gilles Dutilh, and Alexandra Sarafoglou. The creativity-verification cycle in psychological science: new methods to combat old idols. Perspectives on Psychological Science, 13(4):418–427, 2018.
Andrew Ward and Traci Mann. Control yourself: broad implications of narrowed attention. Perspectives on Psychological Science, 17(6):1692–1703, 2022.
Ronald L Wasserstein and Nicole A Lazar. The ASA's statement on p-values: context, process, and purpose. The American Statistician, 70(2):129–133, 2016.
GN Wilkinson and CE Rogers. Symbolic description of factorial models for analysis of variance. Journal of the Royal Statistical Society Series C: Applied Statistics, 22(3):392–399, 1973.
Tal Yarkoni. The generalizability crisis. Behavioral and Brain Sciences, 45:e1, 2022.
Ulrike Zetsche, Paul-Christian Buerkner, and Babette Renneberg. Future expectations in clinical depression: biased or realistic? Journal of Abnormal Psychology, 128(7):678, 2019.
Sam Zhang, Patrick R Heck, Michelle N Meyer, Christopher F Chabris, Daniel G Goldstein, and Jake M Hofman. An illusion of predictability in scientific results: even experts confuse inferential uncertainty and outcome variability. Proceedings of the National Academy of Sciences, 120(33):e2302491120, 2023.