As a general rule, Bayesians prefer to encode assumptions directly into the model by using different priors and likelihoods rather than through ad hoc heuristics such as outlier removal rules.
One very useful option when dealing with outliers and Gaussian distributions is to replace the Gaussian likelihood with a Student's t-distribution. This distribution has three parameters: the mean, the scale (analogous to the standard deviation), and the degrees of freedom, which is usually referred to using the Greek letter 
, that can vary in the interval [0, ∞]. Following Kruschke's nomenclature, we are going to call the
