Home Statistics and Visualization Statistical Modeling, Causal Inference, and Social Science Which books, papers, and blogs are in the Bayesian canon?

Which books, papers, and blogs are in the Bayesian canon?

Statistical Modeling, Causal Inference, and Social Science 2024-08-22

Inspired by this effort by Patrick Collison for Silicon Valley [linked from Tyler Cowen], I thought that it might be fun to think about what makes up the Bayesian canon. As Collison said, “This isn’t the list of books that I think one ought to read — it’s just the list that I think roughly covers the major ideas that are influential here.” I’ve made a start. Would should be added/removed? Books:

  • Berger, James O. (1985). Statistical Decision Theory and Bayesian Analysis. Springer.
  • de Finetti, Bruno. (1974). Theory of Probability: A Critical Introductory Treatment. Wiley.
  • Gelman, Andrew, and Jennifer Hill. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
  • Gelman, Andrew, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. (2013) [1995]. Bayesian Data Analysis. Chapman & Hall/CRC.
  • Jaynes, E.T. (2003). Probability Theory: The Logic of Science. Cambridge University Press.
  • Jeffreys, Harold. (1998) [1939]. Theory of Probability. Oxford University Press.
  • McElreath, Richard. (2020) [2015]. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. CRC Press.
  • Savage, Leonard J. [1974] (1954). The Foundations of Statistics. Wiley.
  • Stigler, Stephen M. (1986). The History of Statistics: The Measurement of Uncertainty Before 1900. Belknap Press of Harvard University Press.
  • Tukey, John. (1977). Exploratory Data Analysis. Addison-Wesley

Articles:

  • Bayes, Thomas. (1763). “An Essay towards Solving a Problem in the Doctrine of Chances.” Philosophical Transactions of the Royal Society of London, 53, 370–418.
  • Betancourt, Michael. (2017). “A Conceptual Introduction to Hamiltonian Monte Carlo.” arXiv. https://arxiv.org/abs/1701.02434.
  • Fienberg, Stephen E. (2006). “When Did Bayesian Inference Become ‘Bayesian’?” Bayesian Analysis, 1(1), 1–37. https://doi.org/10.1214/06-BA101.
  • Gelman, Andrew, Aki Vehtari, Daniel Simpson, Charles Margossian, Bob Carpenter, Yuling Yao, Lauren Kennedy, Jonah Gabry, Paul-Christian Bürkner, and Martin Modrák. (2020). “Bayesian Workflow.” arXiv. https://doi.org/10.48550/arXiv.2011.01808.
  • Navarro, D. J. (2019). “Between the devil and the deep blue sea: Tensions between scientific judgement and statistical model selection.” Computational Brain and Behavior 2, 28–34, https://doi.org/10.1007/s42113-018-0019-z.
  • Neal, Radford (2011). “MCMC Using Hamiltonian Dynamics”, in Handbook of Markov Chain Monte Carlo, CRC Press. (eds. Stephen Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng), https://www.mcmchandbook.net/HandbookChapter5.pdf.
  • Raftery, Adrian E. (1995). “Bayesian Model Selection in Social Research.” Sociological Methodology, 25, 111–163, https://doi.org/10.2307/271063.
  • Wang, Wei, David Rothschild, Sharad Goel, and Andrew Gelman. (2015). “Forecasting Elections with Non-Representative Polls.” International Journal of Forecasting, 31(3), 980–991. https://doi.org/10.1016/j.ijforecast.2014.06.001.

Blogs/online writing: