When did Bayesian inference become "Bayesian"?

By Stephen E. Fienberg. 2006

400 citations recorded by the source corpus.

While Bayes’ theorem has a 250-year history and the method of inverse probability that flowed from it dominated statistical thinking into the twentieth century, the adjective “Bayesian” was not part of the statistical lexicon until relatively recently. This paper provides an overview of key Bayesian developments, beginning with Bayes’ posthumously published 1763 paper and continuing up through approximately 1970, including the period of time when “Bayesian” emerged as the label of choice for those who advocated Bayesian methods.

Subfields: History and Philosophy of Science, Molecular Biology, Statistics and Probability

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Citations in the corpus

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine (2020, methodology): “However, with the advent of powerful computers and new algorithms (especially the significant contribution of the Markov chain Monte Carlo method), Bayesian methods are still being used in statistics in the 21st century [18,19].”
Bayesian model selection for group studies - Revisited (2014, neutral): “Having said this, established classical and Bayesian techniques may give different answers to the same question — a difference that has entertained generations of statisticians (see e.g., Fienberg, 2006).”
J. B. S. Haldane's Contribution to the Bayes Factor Hypothesis Test (2015, methodology): “or example, Turing used decibans when decrypting the Enigma codes to infer the identity of a given letter in German military communications during World War II (Turing,1941/2012).2 1SeeGood (1988) andFienberg 2006) for a historical review. The term ‘Bayes factor’ comes from Good, who attributes the ”
Perceiving is believing: a Bayesian approach to explaining the positive symptoms of schizophrenia (2009, background): “Probabilistic reasoning is associated with a Bayesian approach to the study of belief formatio”
Structured Pruning for Deep Convolutional Neural Networks: A Survey (2023, methodology): “2) Variational Bayesian: Bayesian inference [244] is a method to infer the posterior probability distribution p ( θ | x ) with the known prior probability distribution p ( θ ) of parameters θ and the observed data x .”
A Survey on Trust Modeling (2015, methodology): “Many belief theories have been proposed since the 1970s, including Bayesian Inference [Fienberg 2006], Dempster-Shafer Theory [Shafer 1976], Transferrable Belief Model [Smets and Kennes 1994], Fuzzy Logic [Zadeh 1965], Subjective Logic [Jøsang 1999, 2001], and Dezert-Smarandache Theory (DSmT)…”
Harold Jeffreys’s Theory of Probability Revisited (2008, background): “Section 1.5 attempts an axiomatic derivation that the Bayesian principles just stated follow the rules 7 As noted by Fienberg (2006), the adjective term “Bayesian” had not yet appeared in the statistical literature by the time Theory of Probability was published, and Jeﬀreys sticks to the 19th…”