class_docstr: Inherits time series methods from TSM class.
  
  **** GAUSSIAN PROCESS NONLINEAR AUTOREGRESSIVE (GP-NARX) MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the time series data that will be used.
  
  ar : int
      Field to specify how many AR terms the model will have.
  
  kernel : kernel object
      For example, SquaredExponential() or OrnsteinUhlenbeck()
  
  integ : int (default : 0)
      Specifies how many time to difference the time series.
  
  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.
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docstr: Creates the negative log marginal likelihood of the model
  
  Parameters
  ----------
  beta : np.array
      Contains untransformed starting values for latent variables
  
  Returns
  ----------
  The negative log marginal logliklihood of the model
docstr_tok: ["Creates","the","negative","log","marginal","likelihood","of","the","model","Parameters","--","--","--","--","--","beta",":","np.array","Contains","untransformed","starting","values","for","latent","variables","Returns","--","--","--","--","--","The","negative","log","marginal","logliklihood","of","the","model"]
code: def full_neg_loglik(self, beta):
      """Creates the negative log marginal likelihood of the model
  
      Parameters
      ----------
      beta : np.array
          Contains untransformed starting values for latent variables
  
      Returns
      ----------
      The negative log marginal logliklihood of the model
      """
      parm = np.array(
          [
              self.latent_variables.z_list[k].prior.transform(beta[k])
              for k in range(beta.shape[0])
          ]
      )
      L = self._L(parm)
      return -(
          -0.5 * (np.dot(np.transpose(self.data), self._alpha(L)))
          - np.log(np.diag(L)).sum()
          - (self.data.shape[0] / 2.0) * np.log(2.0 * np.pi)
      )
  
imports: import sys
  import numpy
  import pandas
  import scipy.linalg
  import scipy.sparse
  import scipy.stats
  from scipy.stats import multivariate_normal
  from ..None import arma
  from ..None import output
  from ..None import tests
  from ..None import tsm
  from ..None import data_check
  from .kernels import *
label_desc: Use image node_id 9 for calling the GPNARX obj's underlying member method code with example usage: obj.full_neg_loglik(beta) and returns: unknown