Creates Markov blanket for the variance latent variables

        Parameters
        ----------
        beta : np.array
            Contains untransformed starting values for latent variables

        alpha : np.array
            A vector of states

        Returns
        ----------
        Markov blanket for variance latent variables

def evo_blanket(self, beta, alpha):
        """ Creates Markov blanket for the variance latent variables

        Parameters
        ----------
        beta : np.array
            Contains untransformed starting values for latent variables

        alpha : np.array
            A vector of states

        Returns
        ----------
        Markov blanket for variance latent variables
        """                 

        # Markov blanket for each state
        evo_blanket = np.zeros(self.state_no)
        for i in range(evo_blanket.shape[0]):
            evo_blanket[i] = self.state_likelihood_markov_blanket(beta, alpha, i).sum()

        # If the family has additional parameters, add their markov blankets
        if self.family_z_no > 0:
            evo_blanket = np.append([self.likelihood_markov_blanket(beta).sum()]*(self.family_z_no),evo_blanket)

        return evo_blanket