@classmethod
def _marginal_log_likelihood(
█▘  cls,
██  bias_var:▚float,
███▘noise_var: float,
█▚  true_mark_var: float,
██▝ true_mark_mean: float,
█ ▞ assessor_ids: np.ndarray,
███ script_ids: np.ndarray,
█▞▗ assessor_marks: np.ndarray,
) -> float:
█▛  # Remove the mean from marks
██▚ marks_normalized = assessor_marks - true_mark_mean
███▘# Calculate the covariance matrix
▚█▘ K = cls._assessors_mark_covariance(
█▘      script_ids=script_ids,
███▛    assessor_ids=assessor_ids,
███▚█▚  true_mark_var=true_mark_var,
███▖    bias_var=bias_var,
█▚    ▚ noise_var=noise_var,
███▖)
▙▞  # Use GPy positive definite inverse.
█▙█▖Kinv, L, Li, logdet = GPy.util.linalg.pdinv(K)
██▞ # Calculate the marginal log-likelihood
█▖  mah_dist = np.dot(np.dot(marks_normalized.T, Kinv), marks_normalized)
███▚mah_dist = mah_dist[0, 0]  # The results of dots products is an array with shape [1, 1]
█ ▗ ll = -0.5 * (logdet + mah_dist + marks_normalized.shape[0] * np.log(2 * np.pi))
▛▗  return ll
███▚█▚
▙▞