Bayesian network

(redirected from Bayesian neural network)

Bayesian network

A form of artificial intelligence—named for Bayes’ theorem—which calculates probability based on a group of related or influential signs. Once a Bayesian network AI is taught the symptoms and probable indicators of a particular disease, it can assess the probability of that disease based on the frequency or number of signs in a patient.
Mentioned in ?
References in periodicals archive ?
I will monitor the dynamic changes in electrophysiological brain responses, as well as overt anticipatory behaviour, over the course of learning in order to capture the changes of anticipatory responses online, and I will apply Bayesian neural network modelling to infer the neural and cognitive state changes underlying these response changes.
These features was used for training a Bayesian Neural Network used for classification.
The measured outputs after noise filtering were classified with Bayesian Neural Network classifier and with pair-wise classifier.
Thinking differently: Assessing non-linearities in the relationship between work attitudes and job performance using a Bayesian neural network, Journal of Occupational and Organizational Psychology 74: 47-61.
A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer.
Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling, International Journal of Advanced Manufacturing Technology, Vol.
2002) Bayesian neural network learning for repeat purchase
The Appendix contains some technical details on Bayesian neural network models.
For roughness modelling, based on the data collected by the planned experiment, they apply three methodologies: regression analysis, support vector machines and Bayesian neural network (BNN).
This version offers the latest in neural networking techniques, including a new, cutting- edge Bayesian neural network tool and access to model parameters and weights, which enable users to confidently explore and document results.
Identification of characteristic length of microstructure for second order continuum multiscale model by Bayesian neural networks, Computer Assisted Mechanics and Engineering Sciences 14(2): 183-196.
Some of the classifiers used in the Active Learning Challenge included linear classifiers, non-linear kernels, Naove Bayes, Nearest Neighbors, Neural Networks, Bayesian Network, and Bayesian Neural Networks, Random Forests, and Support Vector Machines.

Full browser ?