Bayesian Benefits for the Pragmatic Researcher

By Eric-Jan Wagenmakers, Richard D. Morey, Michael D. Lee. 2016

307 citations recorded by the source corpus.

The practical advantages of Bayesian inference are demonstrated here through two concrete examples. In the first example, we wish to learn about a criminal’s IQ: a problem of parameter estimation. In the second example, we wish to quantify and track support in favor of the null hypothesis that Adam Sandler movies are profitable regardless of their quality: a problem of hypothesis testing. The Bayesian approach unifies both problems within a coherent predictive framework, in which parameters and models that predict the data successfully receive a boost in plausibility, whereas parameters and models that predict poorly suffer a decline. Our examples demonstrate how Bayesian analyses can be more informative, more elegant, and more flexible than the orthodox methodology that remains dominant within the field of psychology.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9669c421dda25250471ae7a69beaf226236a67f1

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Citations in the corpus

The JASP guidelines for conducting and reporting a Bayesian analysis (2019, unknown): “The credible interval [ L, U ] has a x % probability that the true parameter lies in the interval that ranges from L to U (an interpretation that is often wrongly attributed to frequentist confidence intervals, see Morey, Hoekstra, Rouder, Lee, & Wagenmakers, 2016).”
A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP (2019, unknown): “4 The updating factor—the change from prior to posterior beliefs—is determined by relative predictive performance for the observed data (Wagenmakers et al., 2016).”
Bayes factor design analysis: Planning for compelling evidence (2016, methodology): “situations common in ecology and astronomy, where nature provides data over time and sampling plans do not exist (Wagenmakers et al., 2016).”
How Many Atheists Are There? (2017, background): “Inspection of this posterior enables us to make a number of statements regarding the relative credibility of a number of different estimates (Wagenmakers et al., 2016).”
Bayesian benefits with JASP (2017, unknown): “…construction of prior distributions that obey a series of desiderata, such that these priors are suitable for a reference-style analysis that may be refined when additional information is available (e.g. Bayarri, Berger, Forte, & García-Donato, 2012; for a discussion see Wagenmakers et al., 2016).”