Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan

By John K. Kruschke. 2014

1,912 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/13a0585b79526a85a231c810654b52b2cd7ecb0f

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Citations in the corpus

bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework (2019, background): “An effective sample size of at least 10.000 is recommended if 95% intervals should be computed (Kruschke, 2015).”
Is psychology suffering from a replication crisis? What does "failure to replicate" really mean? (2015, methodology): “Interested readers are referred to Kruschke (2013, 2014) for such examples.”
Multivariate output analysis for Markov chain Monte Carlo (2015, background): “Another standard way of terminating simulation is to stop when the number of effective samples for each component reaches a pre-specified lower bound (see Atkinson et al. (2008), Drummond et al. (2006), Giordano et al. (2015), Gong and Flegal (2016), and Kruschke (2014) for a few examples).”
Bayesian best-worst method: A probabilistic group decision making model (2020, methodology): “Such a technique is used in different Bayesian models as well [50] .”
Bayes factor design analysis: Planning for compelling evidence (2016, methodology): “Concerning the sequential procedures described here, some authors have raised concerns that these procedures result in biased effect size estimates (e.g., Bassler et al. 2010; kruschke 2014).”
The statistical significance filter leads to overoptimistic expectations of replicability (2018, unknown): “practical equivalence” method for decision-making (Kruschke, 2015).”
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty (2020, methodology): “Kruschke [69] defines a method that attempts to simultaneously convey aleatoric and epistemic uncertainty in a single plot by showing the predictive densities resulting from various samples the posterior distribution; however, the efficacy of such plots in practice is still unknown.”
Rejecting or Accepting Parameter Values in Bayesian Estimation (2018, unknown): “HDI limits were computed from the MCMC chain using the method explained in Section 25.2.3 of Kruschke (2015) and with an effective sample size that exceeded 10,000, as recommended in Section 7.5.2 of Kruschke (2015).”
On Using Bayesian Methods to Address Small Sample Problems (2016, methodology): “With sampling-based Bayesian methods such as Markov chain Monte Carlo (MCMC), which is becoming increasingly synonymous with Bayesian estimation in empirical studies, this means that the quality of inference is controlled not by sample size approaching infinity, but rather by the number of samples t”
Psychology, Science, and Knowledge Construction: Broadening Perspectives from the Replication Crisis (2018, methodology): “Bayesian on l y estimation of the posterior distribution of parameter values given new data is informative and can be estimated using u s e methods that are becoming widely available (see, e.g., Kruschke 2015).”
scCODA is a Bayesian model for compositional single-cell data analysis (2021, methodology): “To measure the certainty of scCODA ’ s credible effects, we calculate high-density intervals 36 for each effect parameter β m ; k .”
Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis (2016, methodology): “This method is called Bayesian parameter estimation (Gelman et al., 2014; Kruschke, 2015).”
A global synthesis reveals biodiversity-mediated benefits for crop production (2019, methodology): “For each model, posterior samples were summarized based on the 510 Bayesian point estimate (median), standard error (median absolute deviation), and posterior 511 uncertainty intervals by highest density intervals (HDIs), a type of credible interval which 512 contains the required mass such that all”