Bayesian Cognitive Modeling: A Practical Course

By Michael D. Lee, Eric-Jan Wagenmakers. 2014

1,899 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9bb60de3fe285d70985324a67e45d724c40e3d8f

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Citations in the corpus

A Tutorial on Fisher Information (2017, background): “e posterior distribution is a combination of what we knew before we saw the data (i.e., the information in the prior), and what we have learned from the observations in terms of the likelihood (e.g., Lee and Wagenmakers, 2013). Note that the integral is now over θ and not over the potential outcomes”
Requiem for a Nudge: Framing effects in nudging honesty (2020, unknown): “Whiskers indicate 95% conﬁdence intervals. ysis of the complete data set, the size of the Bayes Factor (BF 01 = 508.319) reveals decisive evidence ( Lee and Wagenmakers, 2014 ) for the null hypothesis of no treatment difference for the subset of participants who passed the attention check.”
Transitive in Our Preferences, But Transitive in Different Ways: An Analysis of Choice Variability (2014, methodology): “Fortunately, there are Bayesian methodologies that are well suited to nonnested model comparison (Lee & Wagenmakers, 2014, chapter 7).”
Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015 (2018, methodology): “In additional Bayesian analyses, we use an errors-in-variables mixture model (28) to estimate the true positive rate in the total sample (see the Supplementary Information, section 2 and Fig.”
Bayesian alternatives for common null-hypothesis significance tests in psychiatry: a non-technical guide using JASP (2018, methodology): “[23] here given its use in JASP, its relevance in many research contexts, and to provide a preliminary frame of”
How Bayes factors change scientific practice (2016, methodology): “An example of Bayes factors motivating a closer consideration of theory is provided byDienes (2015); see also the examples in Lee and Wagenmakers (2014): Sometimes Bayes requires that extra data are gathered on a different condition in order to interpret another condition, data not demanded by…”
Robust Modeling in Cognitive Science (2019, unknown): “The canonical Bayesian approach to model selection is based on the Bayes factor, which is a ratio expressing how much evidence the data provide for one model over another (Kass and Raftery 1995; Lee and Wagenmakers 2013, Chapter 7; Vandekerckhove et al. 2015).”
Quantifying Support for the Null Hypothesis in Psychology: An Empirical Investigation (2018, unknown): “Although there are different traditions for characterizing the strength of evidence indicated by Bayes factors (Schönbrodt, 2015), values lower than 3 are most often interpreted as anecdotal, and values lower than 10 are generally not considered strong evidence (Lee & Wagenmakers, 2013).”
Day‐to‐day individual alpha frequency variability measured by a mobile EEG device relates to anxiety (2023, background): “Based on Lee and Wagenmakers’ (2014) classification scheme, BF01 between 1 and 3 indicates anecdotal evidence supporting the null hypothesis, BF01 between 3 and 10 indicates moderate evidence supporting the null hypothesis, and BF01 > 10 provides stronger support for the null hypothesis.”