Bayesian network

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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.
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Their topics include evolutionary algorithms in the supervision of error-free control, soft computing techniques in spatial databases, fuzzy decision rule construction using fuzzy decision trees and its application to electronic-learning databases, opportunities for database technologies in a Bayesian belief network methodology for modeling social systems in virtual communities, checking integrity constraints in a distributed database, soft computing techniques in content-based multimedia information retrieval, feature selection and variable precision rough set analysis and its application to financial data, a human-machine interface design to control an intelligent rehabilitation robot system, and congestion control using soft computing.
A more recent study described a prototype Bayesian belief network for the diagnosis of acidification in Welsh rivers.
An Algorithm for Bayesian Belief Network Construction from Data, Proc.
The number of full-scaled belief network applications is very small compared to clinical decision support systems developed using other methods.
A sampling of topics: supply chain performance measurement using logical aggregation, a Bayesian belief network modeling of customer behavior on apparel coordination for fashion retailing business, consensus measures for symbolic data, cigarette sensory evaluation classifier predictive control algorithm, uncertainty aversion under distorted probability, and an agent-based approach to modeling small satellite constellations.
Promising probabilistic approaches based on Bayesian Belief Networks (BBN) are currently developed to complement operational deterministic methodologies and tools by contributing to diagnosis accidental situations.
Several of these chapters discuss Bayesian belief networks.
Table 3: World Recent Past, Current & Future Analysis for Artificial Intelligence by Technology - Artificial Neural Networks, Expert Systems, Belief Networks, Decision Support Systems, Intelligent Software Agents, and Others Technologies Markets Independently Analyzed with Annual Sales in US$ Million for Years 2006 through 2015 (includes corresponding Graph/Chart) II-141
Now, statisticians based at Durham University are using Bayesian Belief Networks to develop a sophisticated tool for testing software programs.
One interesting approach to assessing microbial safety is to use Bayesian Belief Networks (BBNs).
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.
The papers in this section describe the relationship between belief networks and knowledge-based systems along with some specific examples.