code: def log_p_blanket(self, beta):
                       
          states = np.zeros([self.state_no, self.data.shape[0]])
          for state_i in range(self.state_no):
              states[state_i,:] = beta[(self.z_no + (self.data.shape[0]*state_i)):(self.z_no + (self.data.shape[0]*(state_i+1)))]     
          
          return np.append(self.evo_blanket(beta,states),self.markov_blanket(beta,states))
docs: Creates complete Markov blanket for latent variables
  
          Parameters
          ----------
          beta : np.array
              Contains untransformed starting values for latent variables
  
          Returns
          ----------
          Markov blanket for latent variables