Statistical Rethinking: A Bayesian Course with Examples in R and Stan

By R. Mcelreath. 2015

2,289 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/79f998d7529a397a0b6bb490c5f67a4a26fdb0e9

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Citations in the corpus

Bayesian alternatives for common null-hypothesis significance tests in psychiatry: a non-technical guide using JASP (2018, background): “The Bayesian framework [9, 10] offers an al-”
Analyzing mixing systems using a new generation of Bayesian tracer mixing models (2018, methodology): “For a set of candidate models fit to the same mixture data, we can calculate the relative support for each model using LOO and Akaike weights, which are estimates of the probability that each model will make the best predictions on new data (Burnham & Anderson, 2002; McElreath, 2016).”
Best practice guidance for linear mixed-effects models in psychological science (2019, background): “An increasing number of researchers advocate the adoption of Bayesian analysis methods (Kruschke, 2013; McElreath, 2015) in which estimates for fixed effects coefficients and random effects variances (or covariances) are associated with posterior distributions that allocate varying probabilities to ”
Heavy quarkonium in extreme conditions (2019, unknown): “[87, 88], for statistical inference see e.”
Meta-Analysis of Empirical Estimates of Loss-Aversion (2020, methodology): “ME, R. (2016): Statistical Rethinking: A Bayesian Course with Examples in R and Stan, CRC Press. N, W. S. (2002): “Comparative Risk Sensitivity with Reference-Dependent Preferences,” Journal of Risk and Uncertainty, 24, 131–142.”
Who teaches children to forage? Exploring the primacy of child-to-child teaching among Hadza and BaYaka Hunter-Gatherers of Tanzania and Congo (2020, methodology): “We assessed convergence through the R-hat Gelman and Rubin convergence diagnostic (McElreath, 2015).”
A parametric texture model based on deep convolutional features closely matches texture appearance for humans (2017, background): “The model’s belief about the data is represented by the full posterior distribution, which can be summarised into arbitrary intervals (see McElreath, 2016, p.58 for related discussion).”
Sampling in software engineering research: a critical review and guidelines (2020, background): “Others argue that “sampling” implies statistical generalization whereas qualitative research involves analytical generalization (Yin 2018; van Manen 2016).”
Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses (2019, methodology): “Weakly informative priors serve as a method of statistical regularization, preventing the winner’s curse by shrinking parameter estimates towards zero unless there is sufficiently strong evidence of a large effect (McElreath 2015).”
Mixed Effects Models are Sometimes Terrible (2017, background): “Interested readers can refer to McElreath (2016) or Kruschke (2014) for book-length introductions to Bayesian statistical methods or to Nicenboim & Vasishth (2016) for an article-length introduction with a linguistic focus.”
A global synthesis reveals biodiversity-mediated benefits for crop production (2019, background): “The advantage of the Bayesian approach is the 514 possibility not only to estimate expected values for each parameter, but also the uncertainty 515 associated with these estimates (55).”