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).
Lastly, our use of Bayesian Belief Network
modeling is novel, and to our knowledge, we are one of the first to use this highly dimensional and robust artificial intelligence machine learning tool, which allowed us to test our primary and secondary questions under different scenarios while taking into consideration the omnidirectional relationships of target variables and confounders in a joint network.
Luby, "Approximating probabilistic inference in Bayesian belief networks
is NP-hard," Artificial Intelligence, vol.
Visitors to the velvet underground of computing are apt to encounter fuzzy sets, neural networks, genetic algorithms, Bayesian belief networks
, rough sets, and other methodologies of uncertainty.
Cooper, "The Computational Complexity of Probabilistic Inference using Bayesian Belief Networks
", Artificial Intelligence, 42: 393-405, 1990.
Hence, we propose to apply four intelligent classification techniques most used in data mining fields, including Bayesian belief networks
(BBN), nearest neighbor (NN), rough set (RS) and decision tree (DT), to validate the usefulness of software metrics for risk prediction.
One interesting approach to assessing microbial safety is to use Bayesian Belief Networks
The computational complexity of probabilistic inference using Bayesian belief networks
. Artificial Intelligence 42, 2-3, 393-405.
Computer-based models, called Bayesian Belief Networks
, have been developed by Dr Gary Barker at IFR, tel:0160 325 5218, to evaluate information that is relevant to food safety standards.
Approximately probabilistic reasoning in Bayesian belief networks