Bayesian Analysis Reporting Guidelines von Kruschke, John K.

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The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective von Kruschke, John K., Liddell, Torrin M.

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Bayesian Assessment of Null Values Via Parameter Estimation and Model Comparison von Kruschke, John K.

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Bayesian Estimation Supersedes the "t" Test von Kruschke, John K

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Bayesian data analysis von Kruschke, John K.

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Rejecting or Accepting Parameter Values in Bayesian Estimation von Kruschke, John K.

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Humans create more novelty than ChatGPT when asked to retell a story von Breithaupt, Fritz, Otenen, Ege, Wright, Devin R., Kruschke, John K., Li, Ying, Tan, Yiyan

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What to believe: Bayesian methods for data analysis von Kruschke, John K

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Bayesian data analysis for newcomers von Kruschke, John K., Liddell, Torrin M.

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Decision-making in stimulant and opiate addicts in protracted abstinence: evidence from computational modeling with pure users von Ahn, Woo-Young, Vasilev, Georgi, Lee, Sung-Ha, Busemeyer, Jerome R, Kruschke, John K, Bechara, Antoine, Vassileva, Jasmin

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The Time Has Come: Bayesian Methods for Data Analysis in the Organizational Sciences von Kruschke, John K., Aguinis, Herman, Joo, Harry

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Editorial: Bayesian methods for advancing psychological science von Vandekerckhove, Joachim, Rouder, Jeffrey N., Kruschke, John K.

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Serial reproduction of narratives preserves emotional appraisals von Breithaupt, Fritz, Li, Binyan, Kruschke, John K.

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Bayesian learning theory applied to human cognition von Jacobs, Robert A., Kruschke, John K.

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

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Ostracism and Fines in a Public Goods Game with Accidental Contributions: The Importance of Punishment Type von Liddell, Torrin M., Kruschke, John K.

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Lay Evaluations of Police and Civilian Use of Force: Action Severity Scales von Celestin, Bradley D., Kruschke, John K.

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Locally Bayesian Learning With Applications to Retrospective Revaluation and Highlighting von Kruschke, John K

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Posterior predictive checks can and should be Bayesian: Comment on Gelman and Shalizi, 'Philosophy and the practice of Bayesian statistics' von Kruschke, John K.

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Bayesian approaches to associative learning: from passive to active learning von Kruschke, John K

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