This digital event focused on the role of Bayesian statistics and modelling in psychology. The event coincided with the publication of a special issue of the Psychonomic Bulletin & Review dedicated to Bayesian Inference for Psychology. The issue was guest edited by Joachim Vandekerckhove (University of California, Irvine), Jeffrey N. Rouder (University of California, Irvine, and University of Missouri), and John K. Kruschke (Indiana University).
The following posts, listed in their order of publication, contributed to this event:
- Stephan Lewandowsky provided an overview and sketched out how Bayesian statistics can prevent miscarriages of justice and statistical inference.
- Joachim Vandekerckhove provided a brief history of #BayesInPsych.
- Andrew Gelman summarized the four advantages of Bayesian approaches.
- Simon Farrell explained that we often know more than we think, and how that helps us get our priors right.
- Clintin Davis-Stober explored the role of priors further and showed how they can effectively become part of a psychological model.
- Trisha Van Zandt and Brandon Turner revealed how Bayesian methods can be approximated even in cases in which the likelihood of a model cannot be computed directly.
