code: def evidence_from_inversion_terms(chi_squared, regularization_term, log_curvature_regularization_term,
                                    log_regularization_term, noise_normalization):
      
      return -0.5 * (chi_squared + regularization_term + log_curvature_regularization_term - log_regularization_term
                     + noise_normalization)
docs: Compute the evidence of an inversion's fit to the datas, where the evidence includes a number of \
      terms which quantify the complexity of an inversion's reconstruction (see the *inversion* module):
  
      Likelihood = -0.5*[Chi_Squared_Term + Regularization_Term + Log(Covariance_Regularization_Term) -
                         Log(Regularization_Matrix_Term) + Noise_Term]
  
      Parameters
      ----------
      chi_squared : float
          The chi-squared term of the inversion's fit to the observed datas.
      regularization_term : float
          The regularization term of the inversion, which is the sum of the difference between reconstructed \
          flux of every pixel multiplied by the regularization coefficient.
      log_curvature_regularization_term : float
          The log of the determinant of the sum of the curvature and regularization matrices.
      log_regularization_term : float
          The log of the determinant o the regularization matrix.
      noise_normalization : float
          The normalization noise_map-term for the observed datas's noise-map.