def METHOD_NAME(prior_probabilities, evidence_values):
    """
    Compute the model probability given prior probabilities P(M) and evidence values p(data|M).
    Model posterior probability P(M|data) is proportional to p(data|M)P(M). Posterior probabilities sum up to 1 over
    all models. This function is a utility function (static method), called within the run_estimation method.
    **Inputs:**
    :param prior_probabilities: Values of prior probabilities for all models.
    :type prior_probabilities: list (length nmodels) of floats
    :param prior_probabilities: Values of evidence for all models.
    :type prior_probabilities: list (length nmodels) of floats
    **Output/Returns:**
    :return probabilities: Values of model posterior probabilities
    :rtype probabilities: list (length nmodels) of floats
    """
    scaled_evidences = [evidence * prior_probability for (evidence, prior_probability)
                        in zip(evidence_values, prior_probabilities)]
    return scaled_evidences / np.sum(scaled_evidences)