In real world networks it is often the case that many fits of an SBM have a roughly equal posterior likelihood . Thus these models are equally valid and should also be taken into account. Therefore the second approach samples across the entire posterior likelihood distribution instead. Employing a Bayesian framework this can be achieved by simply taking all model fits and weighting them according to their posterior probability. The entire model classes should be compared by evaluating the so called model evidence by summing over all hierarchical partitions (8):