 Their association with this group of models derives mainly from the architectural affinity with the basic autoencoder (the final training objective has an encoder and a decoder), but their mathematical formulation differs significantly.VAEs are directed probabilistic graphical models (DPGM) whose posterior is approximated by a neural network, forming an autoencoder-like architecture.Differently from discriminative modeling that aims to learn a predictor given the observation, "generative modeling" tries to simulate how the data is generated, in order to understand the underlying causal relations.