class_docstr: Inherits time series methods from TSM parent class.
  
  **** AUTOREGRESSIVE INTEGRATED MOVING AVERAGE EXOGENOUS (ARIMAX) MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame
      Field to specify the univariate time series data that will be used.
  
  formula : string
      patsy string notation describing the regression
  
  ar : int
      Field to specify how many AR lags the model will have.
  
  ma : int
      Field to specify how many MA lags the model will have.
  
  integ : int (default : 0)
      Specifies how many times to difference the time series.
  
  family : family object
      E.g pf.Normal(), pf.t(), pf.Laplace()...
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docstr: Calculates the negative log-likelihood of the Normal model for a minibatch
  
  Parameters
  ----------
  beta : np.ndarray
      Contains untransformed starting values for latent variables
  
  mini_batch : int
      Size of each mini batch of data
  
  Returns
  ----------
  The negative logliklihood of the model
docstr_tok: ["Calculates","the","negative","log-likelihood","of","the","Normal","model","for","a","minibatch","Parameters","--","--","--","--","--","beta",":","np.ndarray","Contains","untransformed","starting","values","for","latent","variables","mini_batch",":","int","Size","of","each","mini","batch","of","data","Returns","--","--","--","--","--","The","negative","logliklihood","of","the","model"]
code: def normal_mb_neg_loglik(self, beta, mini_batch):
      """Calculates the negative log-likelihood of the Normal model for a minibatch
  
      Parameters
      ----------
      beta : np.ndarray
          Contains untransformed starting values for latent variables
  
      mini_batch : int
          Size of each mini batch of data
  
      Returns
      ----------
      The negative logliklihood of the model
      """
  
      mu, Y = self._mb_model(beta, mini_batch)
      return -np.sum(
          ss.norm.logpdf(
              Y,
              loc=mu,
              scale=self.latent_variables.z_list[-1].prior.transform(
                  beta[-1]
              ),
          )
      )
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  import scipy.special
  from patsy import dmatrices, dmatrix, demo_data
  from ..None import families
  from ..None import output
  from ..None import tests
  from ..None import tsm
  from ..None import data_check
  from .arma_recursions import arimax_recursion
label_desc: Use image node_id 15 for calling the ARIMAX obj's underlying member method code with example usage: obj.normal_mb_neg_loglik(beta, mini_batch) and returns: unknown