Open AccessDynamic Knowledge Inference Based on Bayesian Network Learning
Author(s) -
Deyan Wang,
Adam Amril Jaharadak,
Ying Xiao
Publication year - 2020
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2020/6613896
Subject(s) - inference , bayesian network , computer science , dynamic bayesian network , node (physics) , bayesian inference , machine learning , artificial intelligence , bayesian statistics , a priori and a posteriori , maximum a posteriori estimation , variable order bayesian network , bayesian probability , data mining , mathematics , maximum likelihood , statistics , engineering , philosophy , structural engineering , epistemology
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.