def sample(self, nsims=1000):
        """ Samples from the posterior predictive distribution

        Parameters
        ----------
        nsims : int (default : 1000)
            How many draws from the posterior predictive distribution

        Returns
        ----------
        - np.ndarray of draws from the data
        """     
        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)
            sigmas = [self._model(lv_draws[:,i])[0] for i in range(nsims)]
            data_draws = np.array([ss.t.rvs(loc=self.latent_variables.z_list[-1].prior.transform(lv_draws[-1,i]),
                df=self.latent_variables.z_list[-2].prior.transform(lv_draws[-2,i]), scale=np.exp(sigmas[i]/2.0)) for i in range(nsims)])
            return data_draws
Samples from the posterior predictive distribution

        Parameters
        ----------
        nsims : int (default : 1000)
            How many draws from the posterior predictive distribution

        Returns
        ----------
        - np.ndarray of draws from the data