Creates the structure of the model (model matrices etc) for mini batch model

        Here the structure is the same as for _non_normal_model() but we are going to
        sample a random choice of data points (of length mini_batch).

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

        mini_batch : int
            Mini batch size for the data sampling

        Returns
        ----------
        mu : np.ndarray
            Contains the predicted values (location) for the time series

        Y : np.ndarray
            Contains the length-adjusted time series (accounting for lags)

def _mb_non_normal_model(self, beta, mini_batch):
        """ Creates the structure of the model (model matrices etc) for mini batch model

        Here the structure is the same as for _non_normal_model() but we are going to
        sample a random choice of data points (of length mini_batch).

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

        mini_batch : int
            Mini batch size for the data sampling

        Returns
        ----------
        mu : np.ndarray
            Contains the predicted values (location) for the time series

        Y : np.ndarray
            Contains the length-adjusted time series (accounting for lags)
        """     

        rand_int =  np.random.randint(low=0, high=self.data_length-mini_batch-self.max_lag+1)
        sample = np.arange(start=rand_int, stop=rand_int+mini_batch)

        Y = self.data[self.max_lag:][sample]
        X = self.X[:, sample]

        # Transform latent variables
        z = np.array([self.latent_variables.z_list[k].prior.transform(beta[k]) for k in range(beta.shape[0])])

        # Constant and AR terms
        if self.ar != 0:
            mu = np.matmul(np.transpose(X),z[:-self.family_z_no-self.ma])
        else:
            mu = np.ones(Y.shape[0])*z[0]
            
        # MA terms
        if self.ma != 0:
            mu = arima_recursion(z, mu, self.link(mu), Y, self.max_lag, Y.shape[0], self.ar, self.ma)

        return mu, Y