code: def evo_blanket(self, beta, alpha):
                           
  
          # 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
docs: 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