An inference technique which provides a framework for reasoning despite uncertainty, based on the theory of probability. In a Bayesian belief network, each fact/assertion in the knowledge base is represented by a node.
Matthew Kiefer, Rustin Laycock, Shane Axness, Thomas Wang, NPS/NAVAIR Experimental UAS IFC Challenge - Phase III - Bayesian Belief Network Implementation, System Engineering Capstone Report, Naval Postgraduate School, Monterey, CA, USA, March 2015
In particular, a hazard and risk analysis tool exploiting Bayesian belief networks was developed and demonstrated in support of the interim flight clearance process for the the JACKAL VTOL UAS platform being developed for use in Naval Research Laboratory research flight testing.
Learning models in the form of Bayesian belief networks are developed with the help of a heuristic algorithm using the Bayesian function of network structure to distribution matching as a scoring function, named K2 (Jensen, 2001).
Learning Bayesian Belief Networks Based on the MDL Principle: an Efficient Algorithm using the Branch and Bound Technique.
In order to find some relationships between the risk of the software and the metrics extracted from the code of the national project related software system, in this paper, we applied four classical intelligent classification methods which were most used in data mining fields, namely Bayesian belief networks (BBN) [1, 2, 3], nearest neighbor (NN)  rough set (RS) and decision tree (DT) , , to predict the risk of the core source code of a spectrum processing software system which is a "863" National Project related to the Large Sky Area Multi-Object Spectroscopic Telescope (LAMOST) Project in China.
Topics include customized benchmark generation using MDA, predicting change impact in architecture design with Bayesian belief networks, the changing role of architects, and a declarative approach to architectural reflection.