TY - JOUR
A2 - Huang, Chenxi
AU - Wang, Deyan
AU - AmrilJaharadak, Adam
AU - Xiao, Ying
PY - 2020
DA - 2020/12/07
TI - Dynamic Knowledge Inference Based on Bayesian Network Learning
SP - 6613896
VL - 2020
AB - 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.
SN - 1024-123X
UR - https://doi.org/10.1155/2020/6613896
DO - 10.1155/2020/6613896
JF - Mathematical Problems in Engineering
PB - Hindawi
KW -
ER -