 In a Bayesian context, this is equivalent to placing a zero-mean normally distributed prior on the parameter vector.An alternative regularized version of least squares is "Lasso" (least absolute shrinkage and selection operator), which uses the constraint that formula_63, the L1-norm of the parameter vector, is no greater than a given value.