Describe the issue linked to the documentation

In the documentation for Bayesian ridge regression the standard deviation for the posterior distribution of the target variable is easily accessed by calling .predict(X, return_std=True).

However, there is no comparable method for accessing standard deviations for the posterior distributions of the learned parameters. The means of the parameters posterior distributions are given by .coef_ , but the corresponding standard deviation is not easily available.

Am I correct that the standard deviation would be given by the square root of the diagonal of the covariance matrix?
np.sqrt(clf.sigma_.diagonal())

Suggest a potential alternative/fix

Provide an example of accessing the standard deviation of the posterior distribution of the learned parameters.