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: Covariance-derived term to construct expectations. See Rasmussen & Williams.
  
  Parameters
  ----------
  L : np.ndarray
      Cholesky triangular
  
  Returns
  ----------
  np.ndarray (alpha)
docstr_tok: ["Covariance-derived","term","to","construct","expectations",".","See","Rasmussen","&","Williams",".","Parameters","--","--","--","--","--","L",":","np.ndarray","Cholesky","triangular","Returns","--","--","--","--","--","np.ndarray","(","alpha",")"]
code: def _alpha(self, L):
      """Covariance-derived term to construct expectations. See Rasmussen & Williams.
  
      Parameters
      ----------
      L : np.ndarray
          Cholesky triangular
  
      Returns
      ----------
      np.ndarray (alpha)
      """
      return la.cho_solve(
          (L.T, True), la.cho_solve((L, True), np.transpose(self.data))
      )
  
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 2 for calling the GPNARX obj's underlying member method code with example usage: obj._alpha(L) and returns: la