def evidence_from_inversion_terms(chi_squared, regularization_term,
    log_curvature_regularization_term, log_regularization_term,
    noise_normalization):
    """Compute the log evidence of an inversion's fit to a dataset, where the log evidence includes a number of terms which
    quantify the complexity of an inversion's reconstruction (see the *inversion* module):

    Log Evidence = -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 dataset.
    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 dataset's noise map.
    """
    return -0.5 * (chi_squared + regularization_term +
        log_curvature_regularization_term - log_regularization_term +
        noise_normalization)
