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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For the study published in the journal Nature Research, researchers analysed data from the Missouri river basin from the last 50 years to develop the Bayesian network statistical model.
A Bayesian network is constructed with the 35 indicators selected, which are classified into five different categories: international trade and transport, economy and finance, population and social condition, environment and energy, and institutional and political.
It was also proved that the Bayesian network modeling could be useful in epidemiological research [4].
Specific topics include the automated processing of privacy policies under the European Union general data protection regulation, segmenting US court decisions into functional and issue-specific parts, exploiting causality in constructing Bayesian network graphs from legal arguments, dealing with qualitative and quantitative features in legal domains, and a computational approach to checking the validity of rule-based arguments grounded in cases.
Probabilistic approaches such as Bayesian network analysis are well suited to the AOP framework because, like a Bayesian network, an AOP is an intuitive representation of a graphical model that is a formal representation of a joint probability distribution (Koller and Friedman, 2009; Pearl, 2010).
Bayesian Network Model to Evaluate the Effectiveness of Continuous Positive Airway Pressure Treatment of Sleep Apnea.
M4--Dynamic Bayesian Network. Diferente do metodo tradicional, o metodo Dynamic Bayesian Network utiliza a incerteza e variabilidade dos fatores de riscos em conjunto com fatores economicos, de modo a realizar previsoes em diferentes estagios do projeto (Yet et al., 2016).
A novel fault diagnostic method using a dynamic Bayesian network (DBN) allows more generalized inputs from different fault detection results.
This paper proposes a stepwise Bayesian network algorithm for retrieving remote sensing images, aim to address these problems; the schema combines co-occurrence region-based Bayesian network image retrieval with average high-frequency signal strength, and adopts integrated region matching for iterative retrieval, thereby efficiently improving the precision of semantic retrieval and significantly reducing the retrieval time.
Bayesian network is a commonly used tool in probabilistic reasoning of uncertainty in industrial processes [3].
HMM [13] and Bayesian Network [7] are the most widely considered algorithms to solve this problem.

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