def log_likelihood_with_regularization_from(
    *, chi_squared: float, regularization_term: float, noise_normalization: float
) -> float:
    """
    Returns the log likelihood of an inversion's fit to the dataset, including a regularization term which comes from
    an inversion:

    Log Likelihood = -0.5*[Chi_Squared_Term + Regularization_Term + Noise_Term]

    Parameters
    ----------
    chi_squared
        The chi-squared term of the inversion's fit to the dataset.
    regularization_term
        The regularization term of the inversion, which is the sum of the difference between reconstructed
        flux of every pixel multiplied by the regularization coefficient.
    noise_normalization
        The normalization noise_map-term for the dataset's noise-map.
    """
    return float(-0.5 * (chi_squared + regularization_term + noise_normalization))