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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The simple Bayes net is presented in Figure 1 with two independent variables, [X.sub.1] and [X.sub.2], and dependent variable Y with appropriate CPT representing probabilities for each possible state of the nature of variable Y, or event, in the risk assessment terminology.
Based on the results, a prediction model was built with a prediction accuracy of 94.4% with these eleven genes using Bayes net.
A good mix of algorithms have been chosen from these groups that include Bayes Net & Naive Bayes (from Bayes), Multilayer Perceptron, Simple Logistics & SMO (from functions), IBk&KStar (from Lazy), NNge, PART & ZeroR (from Rules) and ADTree, J48, Random Forest & Simple Cart (from Trees).
Each one of these variables was considered (one by one) an outcome variable in the logistic regression models and a class or divergent node in the Bayes net models.
LR-DBN relaxes these assumptions by using logistic regression in a Dynamic Bayes Net. LR-DBN significantly outperforms previous methods on data sets from reading and algebra tutors in terms of predictive accuracy on unseen data, cutting the error rate by half.
The algorithm of the proposed model was coded in Matlab (Mathworks, 2006) by using some of the functions of the Bayes Net Toolbox (Murphy, 2001).
Using learning analytics to identify emergent markers of expertise through automated speech, sentiment and sketch analysis (Marcelo Worsley and Paulo Blikstein); (29) Using Logistic Regression to Trace Multiple Subskills in a Dynamic Bayes Net (Yanbo Xu and Jack Mostow); (30) Monitoring Learners Proficiency: Weight Adaptation in the Elo Rating System (K.
Causal Exclusion and Causal Bayes Nets, ALEXANDER GEBHARTER