def cov_from_value(self, scaling):
        """Return a covariance matrix for the jump distribution using
        the actual value of the stochastic as a guess of their variance,
        divided by the `scaling` argument.

        Note that this is likely to return a poor guess.
        """
        rv = []
        for s in self.stochastics:
            rv.extend(np.ravel(s.value).copy())

        # Remove 0 values since this would lead to quite small jumps...
        arv = np.array(rv)
        arv[arv == 0] = 1.

        # Create a diagonal covariance matrix using the scaling factor.
        return np.eye(self.dim) * np.abs(arv) / scaling