Bayesian network

(redirected from Belief networks)

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 ?
The toolbox consists of tools such as Neural Networks, Fourier Transform, Support Vector Machine, Self-organizing Maps, Fuzzy Logic, Logistic Regression, Hidden Markov Models, Bayesian Belief Networks, Match Matrix, Autoregressive Moving Average, Time-Frequency Analysis, in addition to others.
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.
Recently presented results suggest that further improvement in ASR can be achieved by neural-networks, more precisely, using deep belief networks (DBN) (Deng et.
Van der Meer, "Modelling the reliability of search and rescue operations with Bayesian Belief Networks," Reliability Engineering and System Safety, vol.
The research performed showed that controlled modification of the parameter's value has significant influence on the structure of generated belief networks.
Several of these chapters discuss Bayesian belief networks. Structuring of text data and semantics is addressed in a few chapters.
BNs (also known as Belief Networks, Causal Probabilistic Networks, Graphical Probabilistic Networks and Probabilistic Cause-effect Networks) are powerful tools for knowledge representation and reasoning under uncertainty [11].
(2009) Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations.
Hinton, <<Acoustic modeling using deep belief networks,>> IEEE Transactions on Audio, Speech, and Language Processing, vol.
Uncertainty and Sensitivity Analysis in Bayesian Belief Networks: Applications to Aviation Safety Risk Assessment, International Journal of Industrial and Systems Engineering, Vol.
DRBM has been used to construct the deep belief networks [30], for speech recognition [31, 32], collaborative filtering [33], computational biology [34], and other fields.
-Tony Jebara, Director of Machine Learning Research at Netflix, presents Double-cover Inference in Deep Belief Networks