Bayesian Networks

A Bayesian network consists of a DAG (directed acyclic graph, i.e. a network) together with a conditional probability matrix assigned to each node of the net. One can represent any probability distribution P(x1,x2….) as a Bayesian net, where each variable x1,x2… corresponds to a different node of the graph. Bayesian nets are used to pose and solve inference problems graphically. Bayesian nets generalize Bayes rule, which corresponds to the case of a two node net. A fun way to start learning about Bayesian nets is to download one of the many free or trial-version software applications that implement Bayesian nets, and to go through its tutorial. At the end of this article, you will find a list of such software. Enjoy!