def conditional_cov(self, augmented_data, flat=False):
        # Since the precision is diagonal, we can invert elementwise
        O = augmented_data["omega"]
        T = augmented_data["T"]
        Km1 = self.K-1

        if flat:
            cov = 1./O
        else:
            cov = np.zeros((T, Km1, Km1))
            for t in xrange(T):
                cov[t,:,:] = np.diag(1./O[t,:])

        return cov

### Logistic Normal Models
#   For comparison, we implement the logistic normal model, which is
#   also amenable to PG augmentation, but only the conditional marginals
#   are rendered conjugate with a Gaussian prior, not the conditional joint
#   distribution over \psi_{1:K}.
dataset/ETHPy150Open HIPS/pgmult/pgmult/distributions.py/PGMultinomialRegression.conditional_cov/original