BNET Procedure

The BNET procedure learns a Bayesian network from an input data table in SAS Viya. A Bayesian network is a directed acyclic graphical model in which nodes represent random variables and the links between nodes represent direct dependency among random variables. Variables that are connected through intermediary variables in the network are conditionally independent given the intermediary variables. Because the Bayesian network provides conditional independence structure and a conditional probability table at each node, the model has been used successfully as a predictive model in supervised data mining. For more information about Bayesian networks, see Pearl (1988).

The BNET procedure can learn different types of Bayesian network structures, including naive, tree-augmented naive (TAN), Bayesian network-augmented naive (BAN), parent-child Bayesian network, general Bayesian network, and Markov blanket. PROC BNET performs efficient variable selection through independence tests, and it selects the best model automatically from the specified parameters. It also generates SAS DATA step code or an analytic store to score data. It can load data from multiple nodes and perform computations in parallel.