Readings in Bayesian

Highly recommend :
Recommend :
Only for diehards :

Motivations for Bayesian Methods

NHST / p-hacking

Why most published research findings are false (Ioannidis 2005)
Statistical Evidence in Experimental Psychology: An Empirical Comparison Using 855 t Tests (Wetzels et al. 2011)
The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time (Gelman and Loken 2013)
The New Statistics: Why and How (Cumming 2014)
Fair Statistical Communication in HCI (Dragicevic 2016)
When Null Hypothesis Significance Testing Is Unsuitable for Research: A Reassessment (Szucs and Ioannidis 2017)
Abandon Statistical Significance (McShane et al. 2019)
Moving to a World Beyond “p < 0.05” (Wasserstein, Schirm, and Lazar 2019) ¹
Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise (Rafi and Greenland 2020)
Why p-values should be interpreted as p-values and not as measures of evidence (Lakens 2021)

Bayes in Practice

DAGs / Causal Inference

Textbooks

References

Albers, Casper J, Henk AL Kiers, Don van Ravenzwaaij, and Victoria Savalei. 2018. “Credible Confidence: A Pragmatic View on the Frequentist Vs Bayesian Debate.” Collabra: Psychology 4 (1).

Cinelli, Carlos, Andrew Forney, and Judea Pearl. 2020. “A Crash Course in Good and Bad Controls.” Available at SSRN 3689437.

Cumming, Geoff. 2014. “The New Statistics: Why and How.” Psychological Science 25 (1): 7–29.

Davidson-Pilon, Cameron. 2015. Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference. Addison-Wesley Professional.

Downey, Allen. 2021. Think Bayes: Bayesian Statistics in Python. 2nd ed. " O’Reilly Media, Inc.".

Dragicevic, Pierre. 2016. “Fair Statistical Communication in HCI.” In Modern Statistical Methods for HCI, 291–330. Springer.

Gelman, Andrew, and Eric Loken. 2013. “The Garden of Forking Paths: Why Multiple Comparisons Can Be a Problem, Even When There Is No ‘Fishing Expedition’ or ‘p-Hacking’ and the Research Hypothesis Was Posited Ahead of Time.” Department of Statistics, Columbia University 348.

Harrell, Frank. 2021. “My Journey from Frequentist to Bayesian Statistics.” Statistical Thinking. https://www.fharrell.com/post/journey/.

Heiss, Andrew. 2020. “Ways to Close Backdoors in DAGs.” https://www.andrewheiss.com/blog/2020/02/25/closing-backdoors-dags/.

———. 2021. “A Guide to Working with Country-Year Panel Data and Bayesian Multilevel Models.” https://www.andrewheiss.com/blog/2021/12/01/multilevel-models-panel-data-guide/.

Ioannidis, John PA. 2005. “Why Most Published Research Findings Are False.” PLoS Medicine 2 (8): e124.

Jaynes, Edwin T. 2003. Probability Theory: The Logic of Science. Cambridge university press.

Jaynes, Edwin T, and Oscar Kempthorne. 1976. “Confidence Intervals Vs Bayesian Intervals.” In Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science, 175–257. Springer.

Johnson, Alicia A, Miles Q Ott, and Mine Dogucu. n.d. “Bayes Rules!: An Introduction to Applied Bayesian Modeling.”

Lakens, Daniel. 2021. “Why p-Values Should Be Interpreted as p-Values and Not as Measures of Evidence.” http://daniellakens.blogspot.com/2021/11/why-p-values-should-be-interpreted-as-p.html.

Martin, Osvaldo A, Ravin Kumar, and Junpeng Lao. 2021. “Bayesian Modeling and Computation in Python.”

McShane, Blakeley B, David Gal, Andrew Gelman, Christian Robert, and Jennifer L Tackett. 2019. “Abandon Statistical Significance.” The American Statistician 73 (sup1): 235–45.

Meent, Jan-Willem van de, Brooks Paige, Hongseok Yang, and Frank Wood. 2018. “An Introduction to Probabilistic Programming.” arXiv Preprint arXiv:1809.10756.

Nalborczyk, Ladislas, Paul-Christian Bürkner, Donald R Williams, and Victoria Savalei. 2019. “Pragmatism Should Not Be a Substitute for Statistical Literacy, a Commentary on Albers, Kiers, and van Ravenzwaaij (2018).” Collabra: Psychology 5 (1).

Nicenboim, Bruno, DJ Schad, and Shravan Vasishth. 2021. “An Introduction to Bayesian Data Analysis for Cognitive Science.” Under contract with Chapman; Hall/CRC Statistics in the Social and ….

Rafi, Zad, and Sander Greenland. 2020. “Semantic and Cognitive Tools to Aid Statistical Science: Replace Confidence and Significance by Compatibility and Surprise.” BMC Medical Research Methodology 20 (1): 1–13.

Savage, Leonard J. 1972. The Foundations of Statistics. Courier Corporation.

Storopoli, José Eduardo. 2021. “Bayesian Statistics Using Julia and Turing.” Bayesian Stats. https://storopoli.io/Bayesian-Julia/.

Szucs, Denes, and John Ioannidis. 2017. “When Null Hypothesis Significance Testing Is Unsuitable for Research: A Reassessment.” Frontiers in Human Neuroscience 11: 390.

Van Buuren, Stef. 2018. Flexible Imputation of Missing Data. CRC press.

VanderPlas, Jake. 2014. “Frequentism and Bayesianism: A Python-Driven Primer.” arXiv Preprint arXiv:1411.5018.

Wasserstein, Ronald L, Allen L Schirm, and Nicole A Lazar. 2019. “Moving to a World Beyond ‘p< 0.05’.” The American Statistician. Taylor & Francis.

Wetzels, Ruud, Dora Matzke, Michael D Lee, Jeffrey N Rouder, Geoffrey J Iverson, and Eric-Jan Wagenmakers. 2011. “Statistical Evidence in Experimental Psychology: An Empirical Comparison Using 855 t Tests.” Perspectives on Psychological Science 6 (3): 291–98.

This is the editorial for the 2019 special issue of AMA articles↩︎

Last updated on Feb 14, 2022