Dynamic Knowledge Inference Based on Bayesian Network Learning

On the basis of studying datasets of students' course scores, we constructed a Bayesian network and undertook probabilistic inference analysis. We selected six requisite courses in computer science as Bayesian network nodes. We determined the order of the nodes based on expert knowledge. Using...

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Bibliographic Details
Published in:Mathematical problems in engineering Vol. 2020; no. 2020; pp. 1 - 9
Main Authors: Wang, Deyan, Xiao, Ying, AmrilJaharadak, Adam
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
John Wiley & Sons, Inc
Subjects:
Algorithms
Artificial intelligence
Bayesian analysis
Datasets
Decision theory
Genetic algorithms
Knowledge
Machine learning
Message passing
Nodes
Parameter estimation
Probabilistic inference
Probability
Probability distribution
Random variables
Statistical analysis
ISSN:1024-123X, 1563-5147
Online Access:Get full text
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Summary:On the basis of studying datasets of students' course scores, we constructed a Bayesian network and undertook probabilistic inference analysis. We selected six requisite courses in computer science as Bayesian network nodes. We determined the order of the nodes based on expert knowledge. Using 356 datasets, the K2 algorithm learned the Bayesian network structure. Then, we used maximum a posteriori probability estimation to learn the parameters. After constructing the Bayesian network, we used the message-passing algorithm to predict and infer the results. Finally, the results of dynamic knowledge inference were presented through a detailed inference process. In the absence of any evidence node information, the probability of passing other courses was calculated. A mathematics course (a basic professional course) was chosen as the evidence node to dynamically infer the probability of passing other courses. Over time, the probability of passing other courses greatly improved, and the inference results were consistent with the actual values and can thus be visualized and applied to an actual school management system.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2020/6613896