Computes posterior predictive p-value

        Parameters
        ----------
        nsims : int (default : 1000)
            How many draws for the PPC

        T : function
            A discrepancy measure - e.g. np.mean, np.std, np.max

        Returns
        ----------
        - float (posterior predictive p-value)

def ppc(self, nsims=1000, T=np.mean):
        """ Computes posterior predictive p-value

        Parameters
        ----------
        nsims : int (default : 1000)
            How many draws for the PPC

        T : function
            A discrepancy measure - e.g. np.mean, np.std, np.max

        Returns
        ----------
        - float (posterior predictive p-value)
        """     
        if self.latent_variables.estimation_method not in ['BBVI', 'M-H']:
            raise Exception("No latent variables estimated!")
        else:
            lv_draws = self.draw_latent_variables(nsims=nsims)
            mus = [self.smoothed_state(self.data, lv_draws[:,i])[0][0][:-1] for i in range(nsims)]
            data_draws = np.array([np.random.normal(mus[i], np.sqrt(self.latent_variables.z_list[0].prior.transform(lv_draws[0,i])), mus[i].shape[0]) for i in range(nsims)])
            T_sims = T(self.sample(nsims=nsims), axis=1)
            T_actual = T(self.data)
            return len(T_sims[T_sims>T_actual])/nsims