Harold Jeffreys’s Theory of Probability Revisited
By Christian P. Robert, Nicolas Chopin, Judith Rousseau. 2008
154 citations recorded by the source corpus.
Published nearly seventy years ago, Jeffreys' Theory of Probability (1939) has had a unique impact on the Bayesian community and is now considered to be one of the main classics in Bayesian Statistics as well as the initiator of the objective Bayes school. In particular, its advances on the derivation of noninformative priors as well as on the scaling of Bayes factors have had a lasting impact on the field. However, the book reflects the characteristics of the time, especially in terms of mathematical rigorousness. In this paper, we point out the fundamental aspects of this reference work, especially the thorough coverage of testing problems and the construction of both estimation and testing noninformative priors based on functional divergences. Our major aim here is to help modern readers in navigating in this difficult text and in concentrating on passages that are still relevant today.
Subfields: Statistical and Nonlinear Physics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/536347fb5e12ef959b07d6ec38ab5ba0db238c94
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Citations in the corpus
- J. B. S. Haldane's Contribution to the Bayes Factor Hypothesis Test (2015, background): “In a recent discussion of Jeffreys’s contribution to Bayesian inference, Robert, Chopin and Rousseau (2009) recall the importance and novelty of Jeffreys’s significance tests, If the hypothesis to be tested is H0 : θ = 0, against the alternative H1 that is the aggregate of other possible values [of ”
- A Bayesian Framework for Information-Theoretic Probing (2021, methodology): “(11)This posterior predictive distribution is equivalent to Laplace smoothing (Jeffreys, 1939; Robert et al., 2009).”