Which books, papers, and blogs are in the Bayesian canon?
2024-08-21
Originally posted at Andrew Gelman's blog "Statistical Modeling, Causal Inference, and Social Science" available here.
Inspired by this effort by Patrick Collison, for Silicon Valley, which Tyler Cowen linked to, 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: