What does a Bayes factor look like?

An attempt to illustrate what a Bayes factor looks like, using GIFs.

Checking the asumption of independence in binomial trials using posterior predictive checking

As put by Gelman et al. (2013, page 148): ‘because a probability model can fail to reflect the process that generated the data in any number of ways, posterior predictive p-values can be computed for a variety of test quantities in order to evaluate more than one possible model failure’.

Three methods for computing the intra-class correlation in multilevel logistic regression

In the current post, we present and compare three methods of obtaning an estimation of the ICC in multilevel logistic regression models.

Experimental absenteeism and logistic regression, part II

This post continues our exploration of the logistic regression model by extending it to a multilevel logistic regression model, using the brms package.

Experimental absenteeism and logistic regression, part I

This post aims to assess the average probability of participant presence in psychological experiments and, in the meantime, to introduce Bayesian logistic regression using R and the rethinking package.

Why the Akaike Information Criterion is as much 'Bayesian' as the Bayesian Information Criterion

According to Rubin (1984), a Bayesianly justifiable analysis is one that “treats known values as observed values of random variables, treats unknown values as unobserved random variables, and calculates the conditional distribution of unknowns given knowns and model specifications using Bayes’ theorem”