A benefit of such transformations as f Z is that expectations with respect to the transformed density can be calculated without explicit knowledge of q(z) and without computation of the log-determinant Jacobian terms therefore making computation of the exact likelihood tractable . As a result a normalizing flow-based generative model is trained by maximizing the computed likelihood of the training samples (or equivalently, minimizing their negative log likelihood).