class_docstr: Inherits time series methods from TSM class.
  
  **** GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (GARCH) MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the time series data that will be used.
  
  p : int
      Field to specify how many GARCH terms the model will have.
  
  q : int
      Field to specify how many ARCH terms the model will have.
  
  target : str (pd.DataFrame) or int (np.array)
      Specifies which column name or array index to use. By default, first
      column/array will be selected as the dependent variable.
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 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
      """
  
      sigma2, Y, __ = self._mb_model(beta, mini_batch)
      parm = np.array(
          [
              self.latent_variables.z_list[k].prior.transform(beta[k])
              for k in range(beta.shape[0])
          ]
      )
      return -np.sum(
          ss.norm.logpdf(
              Y,
              loc=parm[-1] * np.ones(sigma2.shape[0]),
              scale=np.sqrt(sigma2),
          )
      )
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  from ..None import families
  from ..None import output
  from ..None import tests
  from ..None import tsm
  from ..None import data_check
  from .garch_recursions import garch_recursion
label_desc: Use image node_id 11 for calling the GARCH obj's underlying member method code with example usage: obj.mb_neg_loglik(beta, mini_batch) and returns: unknown