class_docstr: Base class for Gaussian processes. 
class_docstr_tok: ["Base","class","for","Gaussian","processes","."]
docstr: Computes the log marginal likelihood. 
docstr_tok: ["Computes","the","log","marginal","likelihood","."]
code: def compute_log_marginal_likelihood(self):
      """Computes the log marginal likelihood."""
      Y_centred = self.Y - self.mean_func(self.X)
      ret = (
          -0.5 * Y_centred.T.dot(self.alpha)
          - (np.log(np.diag(self.L))).sum()
          - 0.5 * self.num_tr_data * np.log(2 * np.pi)
      )
      return ret
  
imports: from __future__ import division
  from argparse import Namespace
  from itertools import product
  import sys
  import numpy
  from ..distributions.model import Model
  from ..distributions import continuous
  from ..distributions import discrete
  from ..utils.general_utils import stable_cholesky, draw_gaussian_samples, project_symmetric_to_psd_cone, solve_lower_triangular, solve_upper_triangular
  from ..utils.oper_utils import direct_ft_maximise, pdoo_maximise, random_maximise, random_sample_cts_dscr
  from ..utils.option_handler import get_option_specs, load_options
  from ..utils.reporters import get_reporter
label_desc: Use image node_id 11 for calling the GP obj's underlying member method code with example usage: obj.compute_log_marginal_likelihood() and returns: ret