Prior sensitivity in theory testing: An apologia for the Bayes factor

By Wolf Vanpaemel. 2010

173 citations recorded by the source corpus.

Subfields: Artificial Intelligence, General Decision Sciences, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/9743ae2c83871e737760404d6c78038081403135

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Citations in the corpus

Indices of Effect Existence and Significance in the Bayesian Framework (2019, background): “, sampling algorithm, number of iterations, chains, warm-up) and the impact of prior specification (Kass and Raftery, 1995; Vanpaemel, 2010; Kruschke, 2011), all of which are important parameters of Bayesian statistics.”
Transitive in Our Preferences, But Transitive in Different Ways: An Analysis of Choice Variability (2014, background): “Thus, it is the prior, not the likelihood function, that instantiates the restrictions imposed by each probabilistic specification, and which thereby distinguishes the different models of transitivity from one another (Vanpaemel, 2010).”
The Prior Can Often Only Be Understood in the Context of the Likelihood (2017, background): “Conversely, one could follow [20] and say that the full Bayesian expression of marginal probabilities reveals problems that were latent in these priors all along.”
Bayesian alternatives for common null-hypothesis significance tests in psychiatry: a non-technical guide using JASP (2018, background): “However, they can produce Bayes factors that can be biased towards null models, so they are generally not recommended [21].”
Bayesian Assessment of Null Values Via Parameter Estimation and Model Comparison (2011, unknown): “For a discussion of the importance of using informed priors in model comparison, see Vanpaemel (2010).”
Using Bayes to get the most out of non-significant results (2014, background): “Values close to 0 are most plausible; a useful default for the SD is a typical estimated effect size.”
Harold Jeffreys’s default Bayes factor hypothesis tests: Explanation, extension, and application in psychology (2016, background): “Indeed, the prior offers an opportunity for7 the implementation of substantively different model (Vanpaemel, 2010).”
Bayesian benefits with JASP (2017, unknown): “When the data are consistent with the restriction, the more daring hypothesis should be rewarded, receiving a bonus for parsimony (e.g. Jefferys & Berger, 1992; Klugkist, van Wesel, & Bullens, 2011; Lee & Wagenmakers, 2013, Chapter 7; Myung & Pitt, 1997; Vanpaemel, 2010).”
How Bayes factors change scientific practice (2016, background): “The second consequence of having to specify H1 as well as H0 is that thought must be given to what one’s theory actually predicts (Vanpaemel, 2010).”
Default Bayes factors for ANOVA designs (2012, background): “Because Bayes factors measure the evidence for competing positions, they have been recommended for inference in psychological settings (an incomplete list includes Edwards et al., 1963; Gallistel, 2009; Lee & Wagenmakers, 2005; Mulder, Klugkist, van de Schoot, Meeus, & Hoijtink, 2009; Rouder et al.,”
The philosophy of Bayes’ factors and the quantification of statistical evidence (2016, background): “Arguably, this sensitivity of the Bayes factor to the priors is precisely what one would expect: the priors are included in the evaluation insofar as they have empirical content (see also Vanpaemel, 2010).”
Robust Modeling in Cognitive Science (2019, unknown): “Finally, they can provide stronger tests of competing psychological theories, because the models correspond more closely to the theories and formalize more of the assumptions made by the theories (Vanpaemel 2010).”
blavaan: Bayesian structural equation models via parameter expansion (2015, methodology): “The Bayes factor is sensitive to choice of prior distribution (e.g., Liu and Aitkin 2008; Vanpaemel 2010), so researchers using the Bayes factor are advised to carefully consider their priors.”
A Tutorial on Bayesian Nonparametric Models (2011, background): “In some cases, it may be preferable to place a prior over finite-capacity models and then compare Bayes factors (Kass and Raftery, 1995; Vanpaemel, 2010), or to use selection criteria motivated by other theoretical frameworks, such as information theory (Grünwald, 2007).”