def log_p_blanket(self, beta):
        """ Creates complete Markov blanket for latent variables

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

        Returns
        ----------
        Markov blanket for latent variables
        """             
        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))
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 ) )
Creates complete Markov blanket for latent variables

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

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