code: def mb_neg_loglik(self, beta, mini_batch):
              
  
          rand_int =  np.random.randint(low=0, high=self.data.shape[0]-mini_batch-self.max_lag+1)
          sample = np.arange(start=rand_int, stop=rand_int+mini_batch)
  
          _, _, _, F, v = self._model(self.data[sample],beta)
          loglik = 0.0
          for i in range(0,len(sample)):
              loglik += np.linalg.slogdet(F[:,:,i])[1] + np.dot(v[i],np.dot(np.linalg.pinv(F[:,:,i]),v[i]))
          return -(-((len(sample)/2)*np.log(2*np.pi))-0.5*loglik.T[0].sum())
docs: Creates the negative log likelihood of the model
  
          Parameters
          ----------
          beta : np.array
              Contains untransformed starting values for latent variables
  
          mini_batch : int
              Size of each mini batch of data
  
          Returns
          ----------
          The negative log logliklihood of the model