Understanding the Value of Bayesian Networks - DataScienceCentral.com

Machine learning algorithms are based on correlation – they do not specify cause and effect relations. A Bayesian network is a probabilistic graphical model representing a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Bayesian networks are used for causal inference because they can consider an event and predict the likelihood of several causes that could contribute to the event's occurrence. For example, you could model a disease and its symptoms in a Bayesian network such that you could predict the disease given a set of symptoms. Bayesian networks are directed acyclic graphs (DAGs) whose nodes represent Bayesian variables (as observable quantities, latent variables, unknown parameters or hypotheses).