Abstract Standard likelihood penalties to learn Gaussian graphical models are based on regularizing the off‐diagonal entries of precision matrix. Such methods, and their Bayesian counterparts, not invariant scalar multiplication variables, unless one standardizes observed data unit sample variances. We show that such standardization can have a strong effect inference introduce new family partia...