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

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)
            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)])
            return data_draws