Harold Jeffreys’s default Bayes factor hypothesis tests: Explanation, extension, and application in psychology

By Alexander Ly, Josine Verhagen, Eric-Jan Wagenmakers. 2016

377 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4b70f293a6a73ded7551ef8d88e24da5354df9c1

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Citations in the corpus

bridgesampling: An R Package for Estimating Normalizing Constants (2017, unknown): “We start with a simple statistical example: a Bayesian paired-samples t -test (Jeﬀreys 1961; Rouder et al. 2009; Ly et al. 2016; Gronau et al. 2017a).”
Further Analysis of Outlier Detection with Deep Generative Models (2020, unknown): “The test statistics, having the form of a Bayes factor, and can also be viewed that comparing two competing hypotheses (pmodel and pgeneric) without assuming either is true [22].”
J. B. S. Haldane's Contribution to the Bayes Factor Hypothesis Test (2015, background): “Wrinch and Jeffreys went on to show how the Bayes factor—which is the amount by which the data, q , shift the balance of probabilities for p versus ∼ p—can form the basis of a philosophy of scientific learning (Ly, Verhagen and Wagenmakers, 2016).”
A Tutorial on Fisher Information (2017, methodology): “…as it provides an argument to choose priors on a3 vector-valued parameter independently (e.g., Berger et al., 1998; Huzurbazar, 1950, 1956; Jeffreys,4 1961; Kass and Vaidyanathan, 1992; Ly et al., 2016d,c), see also Cox and Reid (1987); Mitchell1 (1962).2 By taking a random variable X with w = 3…”
Bayesian alternatives for common null-hypothesis significance tests in psychiatry: a non-technical guide using JASP (2018, methodology): “Here, we assigned a stretched beta prior [31], with”
Bayes factor design analysis: Planning for compelling evidence (2016, background): “2015), the inherently subjective nature of this component has caused many Bayesian statisticians to focus on the Bayes factor –the predictive updating factor– as the metric of interest (Hoijtink et al., 2008; Jeffreys, 1961; Kass & Raftery, 1995; Ly et al., 2016; Mulder & Wagenmakers, 2016; Rouder e”
Bayesian Inference for Kendall’s Rank Correlation Coefficient (2018, unknown): “Ly, Verhagen, and Wagenmakers (2016) used a symmetric beta prior distribution (α = β) on the domain [−1,1], that is, p(ρ) = 2 1−2α B(α, α) × (1 − ρ 2)(α−1), ρ ∈ (−1, 1), (7) whereB is the beta function.”
JASP: Graphical Statistical Software for Common Statistical Designs (2019, methodology): “The Bayesian t tests are based on the work of Jeﬀreys (1961, see also Ly et al. 2016; Rouder et al. 2009; Wetzels et al. 2009).”