"Directed graphical models are a type of probabilistic models where all the variables are topologically organized into a directed acyclic graph." Kingma. (2019). An Introduction to Variational Autoencoders. Foundations and Trends in Machine Learning.
"We work with directed probabilistic models, also called directed probabilistic graphical models (PGMs), or Bayesian networks." Kingma. (2019). An Introduction to Variational Autoencoders. Foundations and Trends in Machine Learning.
"The joint distribution over the variables of such models factorizes as a product of prior and conditional distributions" Kingma. (2019). An Introduction to Variational Autoencoders. Foundations and Trends in Machine Learning.
"If all variables in the directed graphical model are observed in the data, then we can compute and differentiate the log-probability of the data under the model, leading to relatively straightforward optimization." Kingma. (2019). An Introduction to Variational Autoencoders. Foundations and Trends in Machine Learning.
