MSE stands for mean squared error, MAE stands for mean absolute error. These are commonly used measurement in modelling.

$\mathrm{MSE}=\frac{1}{n} \sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}$

$\mathrm{MAE}=\frac{1}{n} \sum_{i=1}^{n} \left| y_{i}-\hat{y}_{i} \right|$

$\operatorname{MSE}(\hat{\theta})=\operatorname{Var}(\hat{\theta})+(\operatorname{Bias}(\hat{\theta}, \theta))^{2}$