code: def state_likelihood_markov_blanket(self, beta, alpha, col_no):
               
          _, _, _, Q = self._ss_matrices(beta)
          blanket = np.append(0,ss.norm.logpdf(alpha[col_no][1:]-alpha[col_no][:-1],loc=0,scale=np.sqrt(Q[col_no][col_no])))
          blanket[:-1] = blanket[:-1] + blanket[1:]
          return blanket
docs: Returns Markov blanket of the states given the variance latent variables
  
          Parameters
          ----------
          beta : np.array
              Contains untransformed starting values for latent variables
  
          alpha : np.array
              State matrix
  
          Returns
          ----------
          State likelihood