Bayesian Linear Regression

The main difference with traditional approaches is in the specification of prior distributions for the regression parameters, which relate covariates to a continuous response variable. However, the Bayesian approach also provides a fairly intuitive way to add random effects (such as a random intercept or random slope), which results in what is traditionally known as a linear mixed model.