Mean absolute errorIf we assume the error ϵ=y−y^\epsilon = y - \hat{y}ϵ=y−y^ follows a normal distribution, the log likelihood is logp(ϵ)=−∣ϵ∣b+const\log p(\epsilon) = -\frac{|\epsilon|}{b} +\text{const}logp(ϵ)=−b∣ϵ∣+const. Maximizing this leads to the derivation of MAE.Analogous to assuming Laplace-distributed errors and Less sensitive to outliers.